Method and system for implementing circuit simulators

ABSTRACT

A system and method for performing circuit simulation is described. The present approach provides methods and systems that create reusable and independent measurements for use with circuit simulators. Also disclosed are parallelizable measurements having looping constructs that can be run without interference between parallel iterations. Reusability is enhanced by having parameterized measurements. Revisions and history of the operating parameters of circuit designs subject to simulation are tracked. Mechanisms are provided that allow for viewing, measurement or other manipulation of signals at specific locations in a circuit design for simulation, such as parameters that include observation points which are implemented using probes. One approach to executing a measurement is via a controllable and flexible control statement, which in one embodiment is the “run” statement. Improved interfaces for viewing, controlling, and manipulating simulations and simulation results are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. Utility applicationSer. No. 10/740,372 filed Dec. 17, 2003 and entitled “METHOD AND SYSTEMFOR IMPLEMENTING CIRCUIT SIMULATORS”, which claims the benefit of U.S.Provisional Application Ser. No. 60/434,295 that was filed on Dec. 17,2002 and entitled “METHOD AND SYSTEM FOR IMPLEMENTING CIRCUITSIMULATORS”, the contents of both the U.S. utility application and theU.S. provisional application are hereby incorporated by reference intheir entireties.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files and records, but otherwise reserves all othercopyright rights.

TECHNICAL FIELD

The invention relates to circuit simulators, and more specifically tosystems and methods for implementing circuit simulators.

BACKGROUND

Circuit simulation is a critical aspect in the modern circuit designprocess. The design process is a highly interactive process where thedesigner uses the simulator to explore the circuit. It is important thatthe iterative exploration process of circuit design be as efficient andas natural as possible. It is during the design phase that the designerinteractively runs a simulator to determine the effect of changes fromvarious design parameters and component values on a circuit. Any tediousor repetitive actions required by the environment can distract thedesigner and therefore can result in a waste of the designer's time.

Simulation scripting languages are heavily used by some designersbecause they allow tedious repetitive tasks to be automated, therebyspeeding up the simulation process. However, despite the appeal ofscripting languages, many designers do not use them. One obvious reasonfor this is that languages must be learned before they can be used. Thisis a significant investment in time that many designers do not feel isjustified. The reason is that scripts, once written, tend to beapplicable only in the specific situation they were written for, andwhile it is possible to make them more generic, it generally requiresconsiderable effort and expertise. Writing and keeping track of the manyscripts needed can often result in more overhead than simply running thesimulations interactively. Thus, unless a designer is doing exactly thesame simulations over and over, as they would when doing librarycharacterization, using simulation scripting languages provides aminimal true benefit.

Since, the design process is an iterative process of trial and error. Itis common for designers to follow a promising path for hours, only todetermine that it is a dead end. By this time, the designer may havelost track of all the changes made to the circuit and may havedifficulty returning the circuit to its last good state.

An object of the present inventions to overcome the limitations anddrawbacks of prior circuit simulators. Another object of the inventionis to provide a method and system for reusing measurements for circuitsimulators across different circuit designs and circuit simulations. Afurther object of the present invention is to provide reusablemeasurements that can be utilized to measure common circuit performancemetrics.

Embodiments of the present invention provide methods and systems thatcreate reusable and independent measurements for use with circuitsimulators. Also disclosed are embodiments of parallelizablemeasurements having looping constructs that can be run withoutinterference between parallel iterations. Reusability is enhanced byhaving parameterized measurements. Revisions and history of theoperating parameters of circuit designs subject to simulation aretracked. Mechanisms are provided that allow for viewing, measurement orother manipulation of signals at specific locations in a circuit designfor simulation, such as parameters that include observation points whichare implemented using probes. One approach to executing a measurement isvia a controllable and flexible control statement, which in oneembodiment is the “run” statement. Improved interfaces for viewing,controlling, and manipulating simulations and simulation results arealso provided. Embodiments of measurement aliases are also describedherein.

Other and additional objects, features, and advantages of the inventionare described in the detailed description, figures, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart of a process implemented by a measurementaccording to an embodiment of the invention.

FIG. 2 shows a flowchart of a process performed by a simulator forimplementing state-restoration functionality according to an embodimentof the invention.

FIG. 3 shows an architecture for logging a history of states in asimulator according to an embodiment of the invention.

FIG. 4 illustrates a process for implementing state-restorationfunctionality according to an embodiment of the invention.

FIG. 5 shows an illustrative example of a measurement according to anembodiment of the invention.

FIG. 6 a shows a process for implementing a GUI-based interface for ameasurement according to an embodiment of the invention.

FIG. 6 b depicts an illustrative example of a GUI-based form accordingto an embodiment of the invention.

FIG. 7 a illustrates a plot of a measurement result according to anembodiment of the invention.

FIG. 7 b shows a flowchart of an embodiment of a process for usingdisplay attributes.

FIG. 7 c conceptually depicts distribution of attributes which can beused to create a graph.

FIG. 8 shows an illustrative example of an interactive datasheetaccording to an embodiment of the invention.

FIG. 9 shows a flowchart of a process for multi-defining a function oroperator according to an embodiment of the invention.

FIG. 10 shows a flowchart of a process for implementing a measurementalias according to an embodiment of the invention.

FIG. 11 shows a flowchart of a method for parallel iterative processingaccording to an embodiment of the invention.

FIG. 12 shows a flowchart of a process for defining and using a templateaccording to an embodiment of the invention.

FIG. 13 shows a flowchart of a process for defining and using a probeaccording to an embodiment of the invention.

FIG. 14 shows an architecture for storing conditions according to anembodiment of the invention.

FIG. 15 shows an example circuit element.

FIG. 16 shows a flowchart of an embodiment of a process for implementinga run command.

FIG. 17 shows an example table of label locations according to anembodiment of the invention.

FIG. 18 illustrates an example of trace markers according to anembodiment of the invention.

FIG. 19 illustrates an example of a trace marker attached to a scalaraccording to an embodiment of the invention.

FIG. 20 illustrates an example of a trace with a marker according to anembodiment of the invention.

FIG. 21 illustrates an example of a deltax marker according to anembodiment of the invention.

FIG. 22 illustrates an example of a deltay marker according to anembodiment of the invention.

FIG. 23 illustrates an example of a deltaxy marker according to anembodiment of the invention.

FIGS. 24 a-b illustrate examples of CP markers according to anembodiment of the invention.

FIG. 25 illustrates an example of an intercept point marker according toan embodiment of the invention.

FIG. 26 illustrates an example of an impedance plot for a RF inductoraccording to an embodiment of the invention.

FIG. 27 illustrates an example of a circle marker according to anembodiment of the invention.

FIG. 28 illustrates an example table of mask marker attributes accordingto an embodiment of the invention.

DETAILED DESCRIPTION

The present invention is directed to a method and system forimplementing circuit simulators. In the specific embodiment(s)illustrated herein, the invention is embodied as a framework for asimulation control language (which may be referred to herein as the“Spectre Measurement Description Language,” “SpectreMDL” or “MDL”) thatcan be applied to control circuit simulation products, e.g., the Spectreand Wavescan products available from Cadence Design Systems, Inc. of SanJose, Calif. The Spectre product is a circuit simulator and the Wavescanproduct is a simulation results display tool. It is noted that theinventive concepts described herein are not limited to the exactlanguage constructs and syntaxes illustrated here, and indeed may beimplemented entirely outside of a “language.”

Independent Measurement

In one embodiment, the invention implements an independent “measurement”to perform simulations. As described herein, a measurement is anindependent parameterized function that performs actions for measuringsome aspect of the performance of a circuit. Similar to named proceduresin a traditional programming language, a measurement comprises acollection and sequence of language statements, which in the presentembodiment implements a set of activities to perform simulation.

Typically, a simulator has a large amount of internal state (the circuitand its parameters, environmental conditions such as temperature,simulator options, etc.) and there can exist a set of functions tomanipulate that state and to analyze the circuit. The activities of ameasurement can be generically implemented so that they are sharedbetween users and can be encapsulated making them easy to reuse fordifferent circuit designs. In this way, a measurement can be writtenonce and shared with other designers. The measurement can be tailored toa particular circuit design by specifying the appropriate inputparameter values, which are the stimulus and operating parameters, e.g.voltage, current, power, temperature, frequency, and period, of thecircuit design. For example, a generic bandwidth measurement could beapplied to a particular circuit by specifying the name of the inputsource, the point where the output is to be observed, and the frequencyrange to be used as parameters to the measurement. This is in contrastto conventional scripting languages for simulation which are normallyimplemented with very specific scripts that only apply to very specificsimulation scenarios.

FIG. 1 shows a flowchart of actions that may be performed by ameasurement in one embodiment of the invention. At 101, the measurementperforms actions to configure the test fixture. At 103, the measurementspecifies stimuli for the simulation. This action may comprise, forexample, specifying parameters for the input data values to be used inthe simulation. At 105, the simulation(s) are performed. The resultantdata is analyzed at 107. As discussed in more detail below, the dataanalysis action of 107 may be performed upon the entire output data setafter completion of the simulation, or performed on-the-fly for specificdata outputs during the simulation actions of 105. At 109, the outputresults of the simulation are displayed. The measurement is a repeatablefunction that produces a consistent result assuming that the sameparameters are employed with the same circuit design. Measurements canbe assembled like building blocks to form more complex measurements andcombined to completely characterize a circuit.

In contrast to procedures or functions in traditional programminglanguages, a measurement in embodiments of the invention can beimplemented to be independent from interference from other measurements.This aspect of independence for a measurement is significant because asimulation function can change the state of the simulator, and thereforeit can have side effects that interfere with other simulation functions.For example, consider two simulation functions that can be performed,one that measures the bandwidth of a circuit and the other that measuresoffset versus temperature. The simulator initially starts with thetemperature set to 27° C. If the bandwidth simulation function is runfirst, the bandwidth is measured at 27° C. However, if the offsetsimulation function is run first, and if the offset simulation functiondoes not return the temperature back to its original value, then thebandwidth will be measured at some other temperature. Thus, this lack ofindependence between the simulation functions causes interference,thereby making the simulation activities more prone to error, confusing,and difficult to use.

In conventional programming languages, this type of error may occursince a procedure may not be entirely independent from other procedures.In part, this results because of the way global variables are handled byconventional procedures, in which each procedure may potentially makepermanent changes to a global variable, with these changes persistingeven after the procedure completes it execution. If a circuit parameteris implemented as a global variable, then implementing this type oftypical programming language for circuit simulation may allow oneprocedure to detrimentally interfere with another procedure.

The present embodiment introduces the concept of independent orside-effect-free measurements. During the course of the measurement,every change to the state of the simulator is tracked. At the end of themeasurement, every change is reversed. Thus, the simulator and thecircuit is returned to its original state at the end of the measurement.This makes the language easier to use by freeing both the implementerand the user of measurements from worrying about side effects.

A process for implementing this aspect of an embodiment of the inventionis shown in FIG. 2. At 203, the simulation activity is initiated. Thisis performed, for example, by calling a measurement. At 205, the stateof the simulator and circuit is preserved. This can be accomplished, forexample, by logging the state of each data element that is modified orupdated during the simulation. Alternatively, this can be accomplishedby storing a snapshot of the entire state of the simulator and circuitor by starting up a fresh copy of the simulator. At 207, simulationactivities are performed upon the circuit. At 209, the state of thesimulator and circuit is restored. This can be accomplished by restoringthe state of each changed data value that was stored to the log.Alternatively, the entire state of the simulator and circuit can berestored from the logged snapshot or terminating the simulator.

In the present embodiment, a measurement conceptually comprises aconstruct in an embodied language having statements that specify stimuliand operating parameters and extract specific output and operatinginformation from the circuit simulator for the circuit design.Generally, the output and operating information can be related to anyparameter of circuit operation, e.g. signal characteristics,temperature, frequency, etc.

FIG. 5 illustratively shows an example of a measurement (“Measurementcp1”), which measures 1 dB compression points of a circuit. The firstportion of Measurement cp1 establishes and configures parameters for themeasurement functionality. In the disclosed embodiment, parameters aredeclared similarly to variable declarations in many programminglanguages, except that an input or output qualifiers may be specified.Thus, the parameter declaration may establish the type of each parameterand whether it is being specified by the user on the parameter list, orreturned by the function. In addition, default values units, rangelimits, and descriptions may also be specified. In one approach, thevalue of an input parameter cannot be changed within the body of afunction or measurement. Parameters may be initialized using the valueof any previously defined parameter. If a default value is not given foran input parameter, then that parameter will be specified when callingthe function or measurement, or the designer can be prompted to input avalue.

The foreach loop iteratively performs simulation activities over a rangeof parameter values for the measurement, and is described in more detailbelow. As described in more detail below, the “run” command performs thespecific simulation activity to which the measurement is directed. Poutis the output of the sweep block.

In order to support reuse of measurements, certain restrictions may beplaced on the code contained within measurements to avoid non-portablemeasurements. The measurement can be implemented such that direct accessto the circuit design topology and components from within themeasurement are prevented. In particular, direct access to nodes,instances, models, specific circuit parameters, or the like areprevented in one embodiment of the measurement, while accesses arepermitted to generic circuit parameters, such as temperature. However,pointers to these objects can be passed in as parameters to themeasurement, and so the measurement is given indirect access to thecircuit under test. In this way the measurement can be re-used onanother circuit by simply changing the values specified for theseparameters.

In one embodiment, measurements produce results in the form of datasets.Data saved in export statements inside a measurement are placed indatasets for use outside the measurement. Measurements include both userdefined measurements as well as analyses built into the simulator. Inone embodiment, a dataset contains a collection of results, generally inthe form of scalars, signals, and families of signals. Access to resultswithin a particular dataset could be implemented using identifiersseparated by delimiters.

To maintain the independence of measurements, the dataset outputs ofmeasurements should also be independent. In one embodiment, this isimplemented by storing the dataset for a measurement in a separatestorage location for each measurement. Such storage locations could be,for example, separate or protected locations in a memory cache.

Analyses may create large datasets that contain many signals. Forexample, AC analysis is likely to create a data set that contains thetransfer function from the input source to every node in the circuit.User-defined measurements generally produce smaller datasets. Thesedatasets may contain only the signals that are designated for output andfor export. Furthermore, measurements may contain other measurements. Inone embodiment, as measurements run, the datasets are arrangedhierarchically. Thus, sub measurements deposit their datasets within thedataset of the parent measurement. In this way, all the raw data isavailable for an in-depth investigation if a high-level result appearsanomalous.

A single measurement may generate several datasets. The default dataset,which is named after the measurement in one embodiment, is a pointer tothe primary dataset. The primary dataset contains the output and exportvariables for the measurement. It also contains pointers to the datasetscreated by sub-measurements. For example, the example compression pointmeasurement of FIG. 5 could produce a dataset cp that points to cp:outthat contains the output variable (e.g., cp), the export signal (e.g.,Pout), and a pointer to the datasets. A measurement could also createancillary datasets that contain the conditions of the circuit, the inputparameter values for the measurement and the circuit conditions. For theexample, these could be named cp:in and cp:conds.

The analyses may product multiple primary datasets. In this case, thedefault dataset simply refers to one of them. For example, a dc analysisnamed oppt might produce oppt:dc and oppt:op, the first holding the nodevoltages and the second holding the detailed operating point dependentdevice information. In this example, the default dataset oppt mightpoint to oppt.dc. Similarly, a pss analysis named ssResp would producessResp:td, ssResp:fd, and ssResp:tran to hold the time-domain, thefrequency-domain, and the transient responses, and the default datasetssResp would point to ssResp:fd. A mixed-signal transient simulationcould produce two datasets, one for signals from the continuous-timekernel and one for the signals from the signals from the discrete- timekernel.

The datasets could be mutually overlaying. As an example, consider asignal x that is only in the continuous-time dataset, a signal y that isonly in the discrete-time dataset, and a signal z that is in both. Ifthe user makes the continuous-time dataset the active one, and thenplots x, y, and z, then x and z from the continuous-time dataset and yfrom the discrete-time dataset are displayed. In this case, thecontinuous-time dataset overlays the discrete-time dataset. The signalsfrom both are available, but if a signal with the same name is in bothdataset, the signal from the active dataset is used. Similarly, if theuser makes the discrete-time dataset the active one, and again plots x,y, and z, then y and z from the discrete-time dataset and x from thecontinuous time dataset are displayed.

If nodesets, forces, or initial conditions are imposed on an analysis,then in one embodiment, it would also create datasets that contain thesevalues, e.g., which are denoted with the suffixes :nodeset, :force, and:ic.

With measurement aliases (described below), the datasets of the submeasurement will be named after the alias if doing so would not create aconflict with a datasets produced by the alias itself. In this case, thedataset produced by the alias would overlay the one with the same suffixproduced by the sub measurement, and the dataset produced by the submeasurement would retain its hierarchical name. For example, consider ifalias mycp is created for the cp measurement, and assume the aliasincludes additional outputs or exports. In this example, the alias wouldproduce a dataset mycp:out that contains the outputs and exports fromthe alias, and overlays the dataset mycp.cp:out. Thus, listing thesignals in mycp:out show both the outputs and exports of mycp and cp,but listing the signals in mycp.cp:out will only show the outputs andexports of cp. If mycp:out and cp:out both contain a signal of the samename, the one from mycp:out is used if it is the active dataset, and theone from mycp.cp:out is used if it is the active dataset. However, nowconsider creating an alias mypss for the built-in pss analysis. The pssanalysis does not produce a :out dataset so there is no conflict betweenthe datasets produced by the alias and the analysis. In this case, themypss:out is created by the alias to hold its outputs and exports, andmypss:td, mypss:fd, and mypss:tran are produced by the analysis.

A possible exception for the conflicting dataset name rule formeasurement aliases applies when an aliased measurement is an alias ofitself. In this case the primary dataset of the alias replaces theprimary dataset of the sub measurement. If a user defines an alias cpfor the cp measurement, a cp:out dataset is generated for the alias, butthe dataset from the sub measurement, cp.cp:out, is not created.

Besides the datasets run at the top-level, in one embodiment, there is ahierarchical dataset that can be automatically created, e.g., calledprey. When measurements are rerun, the results of the previous run areplaced in prey before the new results are generated. Thus, one canalways compare against the previous results. The user may create theirown datasets by copying existing datasets. Thus, a single dataset mayexist in several different places.

Deleting a dataset causes the link to that dataset to be removed. Whenthere are no more links to a dataset, the dataset is permanentlydeleted. Since a parent dataset contains links to its children, deletinga parent will cause the children to be deleted if there is no otherlinks to the children.

Parallelizable Iterative Block

In many cases, running a simulation is a comparatively expensiveactivity. Total elapsed time will be reduced if the various iterationsof a simulation can be run in parallel. This can be accomplished ifthere are no dependencies between the simulations. The independentmeasurements previously described allow this, but increased parallelismcan be achieved if traditional looping constructs, which are inherentlyserial, can be avoided. The present embodiment of the invention providesan approach for implementing the looping constructs so that theiterations can be run in parallel without interference. The presentapproach can be taken to implement parallel execution for any language,and may be used to implement a general purpose scripting language thatsupports parallel execution.

In one embodiment, parallel execution is enabled by first identifying alist of tasks to be performed. A set of dependencies or task orderingsis specified for the tasks. Alternately, such dependencies or orderingsare inferred or derived. Next, each task is executed in a manner that isindependent of other parallel tasks. This independence applies both tothe input as well as the output of the executed tasks. In other words,the input data values (e.g., a global variable) to the task should notbe dependent upon another parallel task. Similarly, the output of thetask should be independent of other parallel tasks. In one approach, themethod and mechanism described above to implement a measurement can beused to execute tasks in a manner that is independent from other tasks.

In one embodiment, an iterative looping construct is employed thatcaptures all variables that are declared outside the loop and assignedwithin the loop. Access to an index for the loop is removed. Certainvariables that are considered vectors outside the loop are consideredscalars inside the loop. In effect, each parallel iteration of the loopruns in its own environment, free from interference with other paralleliterations. Therefore, each parallel iteration is independent of otherparallel iterations.

FIG. 11 shows a flowchart of a method for parallel iterative processingaccording to an embodiment of the invention. At 1102, the loopprocessing begins. Each parallel iteration begins processing at 1104through 1104 n. For each iteration, actions occur to conceptually createa independent processing environment for the iteration, as explained inmore detail below. Effectively, if there are N number of iterates, therecan be N number of environments that are created to process theseiterates. Vectors that are captured by the loop are resized so that theycontain the same number of elements as there are iterates in the loop.Each element corresponds to an iterate. In this embodiment, an iteratedoes not access any element in the vector except the one that to whichit corresponds. Arrays that are captured by the loop appear to have onelower dimension within the loop than outside the loop (1108). Thatdimension is the one that is resized to have the same number of elementsas iterates in the loop. Thus, a one-dimensional array (e.g., vector)captured by the loop appears to be a scalar inside the loop.

At 1110, each parallel iterate performs the simulation activity. Beforeexiting the loop, scalar values are restored (1112). Scalars that arecaptured by the loop are reset to the value they had when the loop wasentered on every iteration. This is true both for scalar variables fromwith the simulation control language, as well as for circuit parametersand simulator options. They are also returned to their initial valueupon exit from the loop. Scalars that are captured by an incrementalassignment operator (e.g., +=, −=, *=, /=, etc.) are not returned totheir initial value upon exiting the loop. Rather, their final value isthe one that results if all of the incremental operations were performedupon exit from the loop. One embodiment of the invention disallowsmodification of scalars declared outside a loop except for incrementalassignment operations. At the end of the parallel iterations, theresults can be combined back together (1114). Of course, each iterationin the loop can also be processed serially. Since each iterate can beprocessed independently from other iterates, a combination of serial andparallel processing can be used to process the iterates in the loop.

Described now are specific examples of mechanisms that can be utilizedto allow the user to specify that tasks should be run in parallel. Oneexample mechanism that can be used to specify that tasks are run inparallel is the “run” command. The run command is a mechanism to executea measurement or function, and is described in more detail in asubsequent section of this document. The following is an example of arun statement that is used to specify the execution of parallel tasks:

run inl, dnl, sfdr, foreach corner

The “foreach” symbol identifies a looping construct and cornerrepresents an unordered list of conditions, both of which are describedin more detail below. Essentially, this example statement indicates thatthree tasks, named inl, dnl, and sfdr, should be run in parallel.Furthermore, each task should be run for every corner, and each of thosecan run in parallel. If there are 100 corners, then this represents 300independent tasks that can all be run in parallel. This example suggeststhat there are two ways in which to specify parallel execution in a runstatement, to specify multiple tasks and to specify a list of conditionsover which the tasks should be applied. To support this, functions canbe provided that make it easy to build useful types of lists. Functionssuch as swp that returns a list of real numbers that cover a particularinterval, as well as functions that search through the design and buildlists of design objects.

The following are illustrative examples of such functions:

run nf, cp, foreach VDD from {1.8, 2.5, 3.2}

In this example, the nf and cp measurements are run while the designvariable VDD is stepped through three values, 1.8, 2.5, and 3.2. In thiscase it is assumed that VDD is used to control the power supply voltagefor the design.

run nf, cp, foreach temp from swp(start=−25, stop=75, step=1)

In this example, the nf and cp measurements are run while the designvariable temp is linearly stepped from −25 to 75 in increments of 1. Inthis case it is assumed that temp is used to control the ambienttemperature of the design.

Simulations can be run either in the foreground or in the background.When run in the foreground, the simulation controller waits until thesimulations are finished before processing any new input. In addition,the user is kept abreast on the progress of the simulations. This isgenerally preferred when running short simulations or when running batchsimulations.

When running simulations in the background, the controller continues toprocess command input but provides only limited access to the runningsimulations and no running summary of the simulator progress. Bydefault, the run command runs simulations in the foreground. To runsimulations in the background, additional control mechanisms areemployed, e.g., an ampersand (&) is added as the last character on therun command. For example,

run acpr &

would run the acpr measurement in the background. Thus, in one approach,the controller will return a prompt before the acpr simulationcompletes. At this point the default dataset is the one being producedby acpr, which is not yet complete. Displaying signals from this datasetwill result in partial results being displayed. Further, these resultswill be automatically updated on a regular basis until the simulationcompletes, at which point the final results are displayed. All of thisactivity also occurs in the background.

With measurements running in the background, it could happen that arequest is made to run a subsequent measurement that would overwrite adataset currently being generated by a background measurement. In thiscase, and error will be issued and the second run command aborted.

If the measurement returns results to a local variable, the value of thevariable will be 'null until the measurement completes. Upon completionof the background simulations, a message is printed and the finalresults are available.

In one embodiment, the current thread of control can be placed in a“wait” state in which further command execution waits on the results ofa background simulation is resumed as soon as the results are availableand complete. This approach could take one or more arguments that may bethe names of either measurements, datasets, or local variables whosevalue depends on a measurement. If a name is given that could representmore than one valid object, such as might occur if a variable, dataset,and/or measurement share the same name, then the command will firstassume the name represents a variable, and if that is not possible itwill assume it is a dataset. The wait approach causes the controller togo to sleep until all of its arguments are available and complete. Thismight actually occur before the run command itself completes.

As noted above, the present embodiment provides the concept of a foreachloop, which is similar to a for loop in C except that every iterationcan safely be evaluated simultaneously. Consider the example Measurementcp1, shown in FIG. 5, which includes the following foreach loop:

export signal Pout foreach src:dbm from swp(start=start, stop=stop) {run pss(fund=fund) real Vfund = V(load)[1] Pout=dBm(Vfund{circumflexover ( )}2/(2*load:r)) “W”) }

This foreach statement causes the real-valued parameter src:dbm to beswept over the range of values between start and stop. Notice that thereis no index variable defined, as there would be in a traditionaliterative block. The lack of an index both simplifies the block andprevents one iterate from accessing the results of another.

The output of the foreach block can be in the form of an array of dataover the range of the loop variable. In the example, Pout is the outputof the sweep block. It is declared to be a signal, in which “signal” isa vector data type. Notice that within the block Pout appears to be ascalar and outside it appears to be an array. This happens whenever avariable that is declared outside a sweep block is assigned a valueinside a sweep block. Within the block, only the value associated withthe current iterate is available. Outside the block, all values areavailable, as is the value of the sweep variable. In this way, eachiterate of the sweep is guaranteed to be independent and so can beevaluated in parallel. In one embodiment, a datatype is employed for a“point” that comprises two values—an x value and a y value—with a signalbeing a vector of points. The foreach loop automatically places the loopindex as the x values.

In one approach of the iterative looping construct, on each iteration,the value of the loop variable for that iterate is attached to theelement associated with that iterate for each vector captured by theloop.

In addition, the iterate index and the total iteration count can beattached to the loop variable as attributes. In this way, identificationcan be made of the index within the environment for each iterate, but toexclude use of the index to access other iterates.

The generated datasets created within the loop can be knitted into ahierarchical family of datasets. Each dataset has its own collection ofproperties, e.g., having a different value as a function of time. Ineffect, the dataset of waveforms or output results are hierarchicallycombined together to add dimensions to the datasets.

This form of an iterative looping construct allows iteration over cornersets (corner sets in the embodied language are described in more detailbelow).

In one embodiment, Foreach loops have three basic forms. The first is

foreach iterate from list { statement_list }

For each entry in the list, the iterate is set to the element value andthe statements in the statement list are evaluated. The iterate is avalid identifier. Within the loop it is treated as a constant and itsvalue once the loop terminates its value is return to the one it hadbefore it was captured by the loop. Any changes made to other variablesor circuit parameters are also reversed upon completion of the loop. Thelist may either be a vector literal, a vector variable, or a vectorvalued function. For example,

foreach VDD from {3.0, 4.0, 5.0} { run tran(stop=100ns) }steps the design parameter VDD through the members of a vector literal.If VDD had the value of 3.2 before the loop, then within the loop itwould take the values of 3, 4, and 5, but once the loop terminated itsvalue would be returned to 3.2. This example could also be written as

real supplies[ ] = {3.0, 4.0, 5.0} foreach VDD from supplies { runtran(stop=100ns) }It could also be written as

foreach VDD from swp(start=3, stop=5, lin=3) { run tran(stop=100ns) }

“Sweep” refers to a function that generates a list. In one embodiment,it supports sweep parameters such as start, stop, center, span, lin,log, step, dec, values, and can be used to create linear or logarithmicsweep ranges. In this example, a power sweep is performed.

signal Pout foreach src:dbm from swp(start=start, stop=stop, log=pts) {run pss( fund=fund ) real Vfund = V(load)[1] Pout=dBm(Vfund2/(2load:r)“W”) }

In an embodiment, when an iterate is explicitly specified in a foreachloop, two attributes are attached to the iterate, index and count. Theyare both integers. The first contains index for the iterate. In otherwords, iterate==list[index]. The second is the number of elements in thelist, or one greater than the maximum index. In this example, an arrayof sources is set up to generate a range of frequencies and powers.

inst src foreach src from ... { int index = src::index src:type = ‘sinesrc:ampl = dBm2i( startpwr + index*deltaP, Rsrc:r ) src:freq =startfreq + index * deltaF src:sinephase = uniform(−360,0) ... }

In this example the index of the iterate is attached to the loopvariable src and is accessible using src::index.

The following is a second form of the foreach loop:

foreach list { statement_list }

This form is like the first except there is no loop variable.

The third form of the foreach loop is as follows:

foreach condition_list { statement_list }

In this form, the list is constrained to be a collection of one or morecondition sets. A condition set could be a group where every member ofthe group is a design quantity or it could be a configuration set. Acondition list is a collection of similar condition sets, and could bein the form of a group vector, a corner set, or a configuration setlist. The statement list is run for each set of conditions in thecondition list. For example,

foreach {fast, typ, slow} { run tran(stop=100ns) }would run the transient analysis on each corner as listed individually,and

foreach process_corners { run tran(stop=100ns) }would run the transient analysis on each corner as listed collectivelyin “process_corners”.

If a run statement is contained in the statement list in a foreach loop,the resulting dataset is organized in the form of a family of data. Whenthere is an iterate, the value of the iterate becomes an abscissa.Otherwise the abscissa is the name of the condition set. If thecondition sets are unnamed, such as when the condition sets are given inthe form of a group vector, then there is no abscissa. Since order ofexecution of the foreach iterates may be arbitrary and unknowable in oneembodiment, actions such as printing, writing, reading, etc. are notallowed within this embodiment of the loop. Also, the run command may besilent within an embodiment of the foreach loop, e.g., it does not printthe measurement results, etc.

Any object that is defined outside the loop and assigned a value insidethe loop is “captured” by the loop. This includes both local variablesand circuit variables. When captured, the size of the object is made toconform to the length of the loop. Thus, if the loop is iteratingthrough a list of N elements, then the object is made to have Nelements, unless it is a scalar, which are described later. Each elementin the object is uniquely associated with an iteration. Within the loop,the name of the captured object is used to refer to the element of theobject associated with the iteration, and not the whole object. Thisapproach to looping prevents access to other iterations from aparticular iteration. This makes the loop completely parallelizable.

If the variable captured by the loop is defined as having an abscissa,then the value of the iterate is assigned to the abscissa. Scalarscaptured by the loop take their initial or external value on everyiteration, and are returned to their initial or external value once theloop terminates.

A more traditional approach for the loop would increment the value of xon ever iteration. That is not the behavior of the foreach loop in oneembodiment because it is a parallelizable loop, meaning that everyiteration could be run simultaneously. Thus, it is not possible for oneiteration to use the result of another.

Scalars that are captured by an incremental assignment operator (+=, −=,=, /=, etc.) are not returned to their initial value upon exiting theloop. Rather, their value is the one that results if all of theincremental operations were performed upon exit.

Search loops also have the basic forms, the first being:

search iterate from list { statement_list } until (condition)

This finds a first member of the list that results in the conditionbeing true. This member is referred to as the target value of theiterate. In the case that the list is given as a vector of values, thesearch begins at the first element of the list and continues towards theend of the list until it finds the first member that results in thecondition being true. An alternative form searches to find the lastmember that is true.

search iterate from list { statement_list } while (condition)

When the search completes, the iterate retains the target value (thevalue that resulted in the condition being satisfied). In addition anyresults produced by the loops in the form of datasets of local variableassignments are those associated with the target iteration. Thus, anyvariables assigned in the loop or datasets created within the loop willbe those that came from the first successful iteration. None of theresults from the any other iterations are available.

The list may be replaced by a one of a set of search functions thatperform more sophisticated searches. For instance, an example searchfunction that could be used is the binary search function. The binarysearch function is used to search for the first member of a range ofvalues that satisfies a logical condition. It is assumed that thecondition is not satisfied at the beginning of the range (start) and issatisfied at the end (stop).

The second form of the search loop is

search condition_list { statement_list } until (condition)

In this form, the list is constrained to be a collection of one or morecondition sets. The first condition set that results in the conditionbeing satisfied is found.

Another loop commonly provided in a circuit simulator is the Monte Carloanalysis. In this analysis, certain values in the design are designatedto be random variables. Then a large number of simulations are performedthat are essentially identical except the values of the random variablesare modified slightly on each run. This is done as a way of modeling thenatural variation that occurs in physically manufactured circuits. MonteCarlo analysis is supported with the addition of the concepts introducedabove for sweep analyses. Consider the following Monte Carlo analysis:

montecarlo (variations=’process, trials=200, moments=’yes, name=’mc) {bw = Bandwidth(start=1MHz, stop=100MHz,sig=V(out)) sr =SlewRate(stop=20ns,sig=V(out)) export bw sr } view histogram(bw) viewhistogram(sr)

The variables bw and sr appear to be scalar variables inside themontecarlo analysis, and arrays outside. This allows all trials to berun in parallel. The index for these arrays is an integer that rangesfrom 0 to 200. Index 0 represents the nominal case. The functionhistogram returns the frequency of occurrence versus deviation from thenominal (if the nominal was requested, otherwise use the mean). Themoments parameter indicates that the statistical moments for all exportvariables should be computed and reported. Initially the mean andstandard-deviation would be generated, though this might eventually beexpanded to include cross correlations.

The Monte Carlo analysis generates a dataset that takes the name givenin the argument list. If no name is given, montecarlo is used. Thisdataset contains the values of the random variable and the datasetsgenerated on each trial. Thus it is possible to plot the outputs versusthe values of the random variables in the form of a scatter plot.

Tracking and Manipulating Simulator History

A circuit designer may follow various paths of design decisions duringthe design process. Designers often get acceptable results and then tryto improve on them. If they fail, they discard the string of designchanges and revert to the circuit that gave them the acceptable results.In other words, they will follow a particular trail until they get whatthey want or determine that it is not leading them where they want togo, at which point they abandon it for some trail they passed upearlier. Eventually they may decide that some earlier point was, infact, the best or acceptable one and come back to it. Or, they may findsomething along the way of value, but later forget where or when theygot it. With current products, designers must remember every change andattempt to manually back them out—this approach is fraught with dangers,since a designer may not be able to remember or to recreate an earlierversion of the design.

A system and method is described that is capable of tracking changesmade to the state of the simulator, which includes in one embodimentchanges to the circuit and to simulator options. Changes can be trackedby maintaining a list or history of changes to the simulator.“Conditions” can be used for delineating between a first and secondstate in the simulator (as described in more detail below, a conditioncan be considered changes to the base circuit).

FIG. 3 shows an architecture for tracking changes according to oneembodiment of the invention. A simulator block 302 is shown thatinteracts with the current state 304 of the simulator. Current state 304may comprise, for example, a portion of memory containing the currentstate of the simulator, circuit, and simulator options. A log 306 ismaintained to track a history of states for the simulator. As eachchange occurs to the state of the simulator, a new entry can be storedin the log 306 to store the corresponding change in state of thesimulator. In one embodiment, each change or associated set of changesto the state of the simulator automatically caused the simulator stateto be saved as an entry in the log 306. Alternatively, the act ofcreating an entry to store the state of the simulator can be manuallyperformed by the designer during appropriate stages in the designprocess.

In the example shown in FIG. 3, the state of the simulator has undergonefour changes in state. At time=0, the simulator had a state value equalto the value y, with an entry 308 a in the log 306 representing thehistory entry for this state. At time=1, the simulator had a state valueequal to the value y1, with an entry 308 b in the log 306 representingthe history entry for this state. Similarly, at times t=2 and t=3, thesimulator had state values of y2 and y3, which are stored in entries 308c and 308 d, respectively. Various approaches may be taken to initiatenew log entries or to separate the entries. In one embodiment, eachchange in the history log is delineated by a new condition.Alternatively, the logged states can be made and/or indexed on atime-based approach. In yet another approach, various milestone orselected savepoints may be used to delineate between log entries.

FIG. 4 illustrates the process of reverting to a saved version of thesimulator or circuit. During an initial time period t, the current state402 a of the simulator has a value x. The history log 404 a at time=tincludes entries for the previous states of the simulator, if any.

At a later time t+1, the updated current state 402 b of the simulatorchanges to x′. The previous state of the simulator, from just prior tothe change in state from x to x′, is stored as an entry 406 in theupdated history log 404 b. Notice that the previous entry 403 is stillmaintained in the history log. As each new change is made to the circuitor simulator, a corresponding entry is added to the list of entries inthe history log. In this manner, an entire chain of entries can bemaintained for saving the states of the various paths of designdecisions that a designer may follow during the design process.

At a later time=t+n, it may be decided to revert the simulator back tothe state of the circuit as of time=t. This reversion is accomplished byrestoring the current state 402 c of the circuit or simulator back tothe recorded state as of time=t, e.g., by restoring the saved statevalues in history log entry 406 that was saved for time=t. In oneembodiment, the entire state of the simulator/circuit is stored in anentry in the history log. In this approach, the reversion occurs bycopying the parameter values stored in the appropriate history log entryback into the current state of the simulator. In an alternateembodiment, only changed values in the state are recorded in a historylog entry. Thus, if a particular condition changes only parameter x,then only the previous value of parameter x is saved in thecorresponding history log entry. The unchanged portions of the simulatorstate are not recorded in the history log entry. In this approach, torestore a prior version of the simulator, the entire chain and sequenceof history log entries leading to the desired state would be restored,to ensure that every change encountered along the way is undone.

Differences between any two versions of the circuit can be displayed, bycomparing the saved states of the two version from the history log.Thus, the current circuit can be compared against any previous betweenprior version of that circuit by identifying and analyzing the savedstate of the prior version against the current state of the circuit.

A simulation controller can be used to that keeps track of all changesmade to the circuit and the simulator for the purpose of displaying orreversing the changes. In one embodiment, the simulation controllercomprises a software module that maintains the contents of the historylog.

The simulation controller performs the act of attaching the list ofchanges in place when a result is generated. When displaying tworesults, the change lists can be used to determine and display thedifferences in the circuit between the two results. In addition,simulation controller can reverse one or more changes saved in thechange list to revert the circuit back to a previous version. The changelist can be used to update the original representation of the circuit,whether in a netlist or taken from a design database.

The present features will significantly improve the efficiency ofdesigners by allowing them to quickly explore the design space byreducing the number of tedious manual operations they must perform andby eliminating the time spent trying to rediscover how a particulardesirable result was achieved.

Links to GUI

Once a measurement is created, an interface can be automatically createdto allow users to quickly specify parameters for the measurement. To besuitable for novice users, information is made available in a graphicaluser interface (GUI) form that describes the input parameters and guidesthe choices for selecting values for the input parameters for the givenfunction. For example, the type of the parameter, any limits on thevalue of the parameter, and a description of the parameter can be usedto generate the form.

FIG. 6 a shows a flowchart of a process for automatically creating aninterface according to an embodiment of the invention. At 602, themeasurement is created. Parameters for the measurement are identified at604. At 606, output parameters are identified for the measurement. At608, an interface form is created that links to the measurement, inwhich fields in the form correspond to the identified parameters to themeasurement.

FIG. 6 b shows an illustrative example of a form 295 that can be createdfor the compression point measurement discussed with respect to FIG. 5.It is noted that the defined parameters for the compression of FIG. 5correspond to fields in the GUI-based form 295 of FIG. 6 b. Any userthat was familiar with a compression point measurement would be able tofill out this form even if the user is not familiar with the particularsimulation language being employed. Notice that ‘choose’ buttons can beprovided for the instance parameters, which allows them to be specifiedvia the schematic if desired.

By implementing such forms, users can employ and reuse measurementswithout being required to learn and train in a simulation controllanguage. Instead, a measurement can be used by anyone, including noviceusers, merely by accessing and entering parameters fields in theGUI-based form.

In one embodiment, self-documenting parameters and parameter fields areemployed (e.g., parameters with range limits, units, text descriptionspossibly partitioned between a short label and a full description, andindication that parameter is expecting a design object that could beselected on the schematic). In addition, the parameters may be grouped,with a common label and description for the group. The GUI can beimplemented to employ results that display themselves. Libraries ofmeasurements (along with library maintenance tools) can be GUI-based inone approach.

Probe and instance parameters can also be employed in the GUI-basedform. As discussed in more detail below, probes allow measurement pointsto be specified as parameters. As such, probes can be identified andassociated with one or more fields in the GUI form for the measurement.Such probes include probes that support single-ended, differential orcommon-mode quantities, as well as probes that support node, terminal orinstance potentials (voltages) and terminal or instance flows(currents).

Display Attributes

In many cases, a simulation in an analog or mixed-signal applicationproduces waveforms upon which further analysis is desired to determinethe actual performance metric of interest. Visual display of thewaveforms plays an important role for the designer when simulating acircuit.

For example, in the measurement illustrated in FIG. 5, which has anoutput graph shown in FIG. 7 a, a loop is used along with a periodicsteady-state (PSS) analysis to produce an array that contains the outputpower versus the input power which is depicted by power curve 240. Inthis example the actual figure of merit desired by the user may be the 1dB compression point 255. This is the point where the gain has droppedby 1 dB from its small signal level. The measurement then returns thecompression point as its output. This represents a very convenientresult that can easily be printed, stored to a local variable, displayedin a table, etc. However, to assure that the compression point ismeasured correctly, users often would like to see the compression pointcalculation illustrated on the power curve 240. Seeing this gives theuser confidence that the calculation was made correctly.

An example of a possible error that may occur is if one does not use theright small signal gain. The point where the small-signal gain iscomputed is illustrated as the dot 245 on the top of the marker 250. Thefact that it is shown on the graph gives the user quick visual feedbackthat allows assurance that the output parameters are in their desiredrange. The graph in FIG. 7 a consists of the raw data, in this case thepower curve, plus a marker that illustrates the calculation. This isillustrated with the compression point, but the concept applies tovirtually all scalar measurements made on waveforms, e.g. rise time,delay, maximum, settling time, intercept point, spurious free dynamicrange, etc.

At a later point in time, the user may wish to re-visualize theseresults. However, in conventional systems, the simulator would berequired to re-execute the simulation to that exact moment to allow thesame display to be duplicated. Re-executing the entire simulation toreproduce a result set can be inefficient and wasteful of resources.Moreover, the prior approaches to compute the scalar result set arecumbersome to implement. This is because in traditional scriptinglanguages, the function that computes the desired scalar result set isdifferent from the function that displays the marker. Thus, the usermust manually transfer the appropriate information from the calculationfunction to the marker function. In addition, the calculation functionmay not make all the necessary information readily available oraccessible.

In one embodiment of the present invention, to make the display ofscalar points more meaningful, the data that describes the marker andgraph can be attached as attribute(s) (“display attribute(s)”) to thescalar result set. When the result is plotted, the display attributescause the marker to be generated automatically. Using such displayattributes provides many benefits. First, generating markers becomeseffortless for the end-user and for the author of the measurement.Second, since the display attributes are carried by the scalar output ofthe measurement, the entire graph with the marker can be generated andvisually displayed without requiring the entire simulation to bere-executed. No additional information is necessary, since the displayattributes travels with the scalar. Even if the scalar result isreturned from the measurement where it was computed, the graph can begenerated without having to manually move around a quantity ofinformation.

FIG. 7 b shows a flowchart of an embodiment of a process forimplementing a display attribute. At 793, the simulation is executed.this performed, for example, by executing a measurement. At 795, thescalar result set is generated. At 797, the display attributes areattached or otherwise associated with the result set. The additionalattributes may be attached to the result by the computation function toallow the user to interactively update the marker, e.g., from the outputgraph depicted in FIG. 7 a. The arguments of the computation functioncould be attached so that it can be reapplied if a subset of theparameters are altered. This may be done by utilizing keyboard or mouseevents to select the points on the curve and perform the computationalfunction with respect to the selected point or points.

The display attributes may comprise information regarding how the datashould be displayed. For example, the units, axis labels, graph titles,grid style, etc. can be attached to the result to assure that it wouldbe displayed in an appropriate and informative manner thus causing theplotting function to utilize this information. Furthermore, if there aremultiple ways of displaying data, attributes may be used to specifywhich way is desired. Consider a complex transfer function, which can bedisplayed as magnitude, phase, or real or imaginary parts, the hiddenattribute can select which of these to use in the graph and also providethe units, axis labels, graph titles, grid style, etc. Since the dataitself it not actually changed, the user can always display it in adifferent manner later. With reference to the graph shown in FIG. 7 a,the display attributes would include, for example, grid displayinformation, marker point information, title information (e.g., “Outputv. Input Power”), display unit information (e.g., “dBm”), etc. FIG. 7 cconceptually depicts distribution of attributes which can be used tocreate a graph.

The display attributes can be manually attached to the scalar resultset. Alternatively, the display attributes are automatically attached tothe scalar result set. In addition, the scalar result set can becompiled by functionals, which are functions that take vectors as inputand return scalars. The functionals can automatically attach theattributes needed to display the results. At 799, the display attributesare then used to generate the visual results.

Display attributes can also be used to reduce the complexity ofmeasurements, since with display attributes the measurements will notneed instructions as to whether to display their output results.Instead, information is attached to the output results so that they canbe displayed without any further processing as part of the measurements.That is, the output results can be manipulated by further programs. Inthis way, the same measurement can be use in a batch mode, where theoutput results would stored and not immediately displayed, and in aninteractive mode, where the output results would be immediatelydisplayed. The displays can be regenerated multiple times withoutrerunning the measurements. Graphs associated with output results thatwere archived perhaps months or years earlier could be easily displayedwithout rerunning the measurements. In addition, this aspect of theinvention allows the output results from the simulation to be used laterin the same design or for other designs. Display attributes can alsoattach information about the measurement that generated a result to theresult. In this way, the result can be updated without re-specifying themeasurement. Information such as the changes in place in the circuitwhen the result was generated, or any error messages that occurred inthe generation of the measurement, could also be attached to the resultas a hidden attribute.

To reemphasize the point, a significant advantage of this approach isthat the display attributes essentially allow one to go backwards intime to extract how a particular result set was achieved without havingto go through the entire simulation process. In this way, for theexample given in FIG. 7 a, users could interactively compute thecompression point for different levels of compression without having tore-run the measurement, which involves an expensive simulation.

Therefore, described is an approach for attaching display attributes todata objects in a simulation control language, either automatically orat user direction. As part of the data structures, e.g., for functionsand commands, information can be embedded to change their behavior basedupon the presence and values of display attributes. For example,computation functions can attach attributes to their return values thatare later interpreted by display commands and functions so as toinfluence how the results are displayed. The functions and measurementscan attach hidden attributes to their return values. Display functionscan be attached to return values of computation functions by way ofdisplay attributes that are evaluated when the result is displayed.Computation functions can attach parameter values to their return valuesso that the functions may be rerun with adjusted parameter values. Inaddition, a function can attach attributes to a return value toassociate particular keyboard or mouse actions to increments oradjustments of particular parameter values. Measurements can attachtheir parameter values to their return values so that the measurementresults can be updated simply by referencing the result and withoutre-specifying the measurement. A list of changes can be attached to thecircuit or the simulator at the time a measurement result was generatedto that result for the purposes of documentation, and perhaps revertingthe circuit and simulator back to the state is had when the results weregenerated.

It should be noted that while described above, the measurements andfunctions can be used without display attributes.

Markers

Markers are annotations added to graphs to illustrate some importantfeature of one or more trace. As noted above, markers can be attached,e.g. to a trace, by way of an appropriate attribute, e.g., a displayattribute. In one embodiment, the appropriate attribute is a groupattribute that includes all the information needed to draw the marker.This section describes examples of markers suitably used in embodimentsof the invention.

Markers take arguments that are used to specify how the text will appearwhen a marker is plotted. These text annotations are referred to aslabels, and in one embodiment, there are three parameters used tocontrol the content and placement of the text: label, dir, and angle.The label argument is a string. If it includes format codes, the valueis interpolated into the label under direction of the format codes. Ifthe label is not explicitly specified, then the default label of “% f”is used, which results in the value being printed along with its nameand units.

Two additional arguments can be used to place the label. The firstargument is named dir (or orient) and is a name that indicates where thelabel is relative to the point. It is one of the following names: 'c,'e, 'ne, 'n, 'nw, 'w, 'sw, 's, and 'se, which represent centered, east,northeast, north, northwest, west, south, and southeast respectively, asshown in FIG. 17. Here, north means above the point, south means belowthe point, east means to the right of the point, and west means left ofthe point. In addition, centered implies middle-center justification,east implies middle-left justification, northeast implies lower-leftjustification, north implies lower-center justification, northwestimplies lower-right justification, west implies middle-rightjustification, southeast implies upper-right justification, southimplies upper-center justification, and southeast implies upper-leftjustification. The second argument is angle, the angle of the text. Itis a real number. An angle of 0 implies that the text flows from left toright and an angle of 90 implies the text flows from bottom to top. Thetext is oriented and justified after it is rotated. Note markers addtext to a graph that is not associated with any particular graphicobject. Normally notes are closed, but they can be opened by the user toexpose the text. A note marker is specified by attaching a marker withtype='note. The text is specified with the text parameter.

Error markers are like note markers in that they attach text to a graphthat is not associated with any particular graphic object. However, bydefault the text is displayed very prominently, though it can be closed.An error marker is specified by attaching a marker with type=' error.The text is specified with the text parameter. It takes an additionalparameter severity that should be set to either 'notice, 'warning, or'error.

A trace marker is text that is associated with a particular trace, butdoes not have a particular location on the trace. The display tool wouldplace it as it sees fit. A trace marker is specified by attaching amarker with type='trace. It takes the standard label parameters. If nolabel parameter is specified, the name of the trace is used as thelabel, which is particularly useful for identifying traces inpreparation for producing a black and white hardcopy. This isillustrated in FIG. 18.

When a trace marker is attached to a trace, in one embodiment, formatcodes are not used to access a value, though it is possible to use codesthat access attributes if the associated attributes exist. It is alsopossible to apply a trace marker to a scalar, in which case the signalfrom which the scalar was derived should be specified to the marker aspart of the markers attribute group. In this case, the format codes thataccess a value can be used. This is useful for attaching labels totraces that describe an important property of that trace, but for whichthere is no obvious location. FIG. 19 shows an example of a trace markerattached to a scalar.

A point marker is text that is associated with a particular point. It issimilar to the trace marker, with the distinction being that thelocation of the marker is specified and fixed and need not be on thetrace. It takes that standard label parameters along with a locationparameter, loc, which is a point. As with the trace marker, it can beattached to a signal or a scalar. With a point marker, the format codesused in the label access values from loc. A point marker is illustratedin FIG. 20.

An x marker would place a vertical line that always spanned the graph ata particular location defined by an x value.

An y marker would place a horizontal line that always spanned the graphat a particular location defined by an y value.

A deltax marker is text that is associated with a the distance in the xdirection between two points. The two points may be on a single trace,or on two different traces. If it is a scalar, the signal or signalsshould be specified with the marker so that they can be plotted with themarker. The two points define the beginning and the ending of theinterval of interest are specified with refloc and loc. The label isspecified with the traditional label parameters. An example deltaxmarker is shown in FIG. 21.

A deltay marker is text that is associated with a the distance in the ydirection between two points. The two points may be on a single trace,or on two different traces. If it is a scalar the signal or signalsshould be specified with the marker so that they can be plotted with themarker. The two points define the beginning and the ending of theinterval of interest are specified with refloc and loc. The label isspecified with the traditional label parameters. An example of the useof a deltay marker is as follows (which is illustrated in FIG. 22):

point min=min(Vout)

point max=max(Vout)

point pp=max−min

view pp::mkr={type='deltay, sig=Vout, refloc=min, loc=max}

A deltaxy marker is text that is associated with a the distance betweentwo points. The two points may be on a single trace, or on two differenttraces. If it is a scalar the signal or signals should be specified withthe marker so that they can be plotted with the marker. The two pointsdefine the beginning and the ending of the interval of interest arespecified with refloc and loc. The label is specified with thetraditional label parameters. An example is shown in FIG. 23.

A compression point, or cp, marker is a specialized marker suitable fordisplaying the compression point on a power transfer curve. A cp markeris specified by attaching a marker with type='cp to either a point or asignal. If attached to a point, the sig parameter should be specified sothat when plotted, the marker is drawn with the appropriate signal. They-value of the point is assumed to be the output power at thecompression point and its x-value is assumed to be the input power atthe compression point. It attached to a signal, then the point should begiven with the loc parameter. It takes that standard label parametersalong with a reference location parameter, refloc, which is theextrapolation point. A cp marker is illustrated in FIG. 24 a. A cpmarker also support gain compression measurements, as shown in FIG. 24b.

A intercept point, or ip, marker is a specialized marker suitable fordisplaying the intercept point on a power transfer curve. A ip marker isspecified by attaching a marker with type='ip to a either a point (theintercept point) or a signal (the power transfer curve of the nth orderdistortion term). If attached to a point, the sig parameter specified sothat when plotted the marker is drawn with the appropriate signal. Inaddition, other parameters specify power transfer curve of thefundamental term. The y-value of the point is assumed to be the outputpower at the intercept point and its x-value is assumed to be the inputpower at the intercept point. It attached to a signal, then the pointshould be given with the loc parameter. It takes label parameters alongwith a reference location parameter, refloc, which is the x value forthe extrapolation points. An order parameter is used to specify theorder of the distortion. A ip marker is illustrated in FIG. 25.

A line marker takes a point and a slope and draws a line across theextent of the graph. One application for a line marker is to annotate alog-log graph of either admittance or impedance show the immittance ofbasic components such as resistors, capacitors, and inductors, as shownin FIG. 26. Line markers allow users to quickly build a model for theinductor. A line marker causes a line segment to be drawn between twopoints. A line marker is specified by attaching a marker with type='lineto a scalar or to a signal. If it is a scalar, a signal should bespecified with the marker using the sig member so that it can be plottedwith the marker. The two points define the beginning and the ending ofthe line segment are specified with refloc and loc. The label isspecified with the traditional label parameters.

A circle marker causes a circle to be drawn with a given center andradius. If it is a scalar, a signal should be specified with the markerusing the sig member so that it can be plotted with the marker. Thecenter of the circle is specified with a point, loc, and the radius witha real number, radius. The label is specified with the traditional labelparameters. An example that uses the circle marker is shown in FIG. 27.This example is used to plot the noise circles for an amplifier. Noisecircles are used to show designers how the noise of an amplifier variesas a function of the source impedance.

It is also possible to apply mask markers to signals, which are boundsthat vary as a function of the abscissa. To do so, a mask attribute isattached to the signal. There are twelve example types of mask markers,'above, 'below, right, left, 'inside, 'outside, 'not_above, 'not_below,not_right, not_left, 'not_inside, and 'not_outside. Each takes a signalas an argument mask that defines a region, as illustrated in the tableof FIG. 28. Plotting a signal that has a mask attribute causes the maskto itself to be plotted and any violations to be highlighted. The maskmay be applied to either a scalar or a trace. When applying a mask to ascalar, the signal from which the scalar was derived should be specifiedto the marker using the sig member of the markers attribute group. Inthis case, the format codes that access a value can be used.

Data Analysis

Analysis of simulation data can occur in at least one of two ways.Either the analysis occurs during the simulation or after thesimulation. Performing analysis “on-the-fly” during the simulation isgenerally preferred if the analysis can be specified before thesimulation occurs, because the data analysis routines can control thesimulator to get better results or terminate the simulation early oncethe desired results have been achieved. For example, when performingcertain types of activities, e.g., FFT analysis, real-time data analysiscan direct the simulator to place time points at the FFT sample pointsto avoid interpolation error. Or, when computing settling time, it canterminate the simulation once the settling time has been determined.

Post-processing or data analysis after the simulation may be moresuitable under certain circumstances, particularly if it is onlypossible to ascertain what data analysis is required after thesimulation is complete. Thus, both types may be required. However, to beefficient, each type of analysis may require a different implementationof the underlying mathematical functions. Analysis during simulationnormally calls for the functions to operate a point-at-a-time as thedata becomes available. This allows control over the simulator in realtime. With post-processing, the data analysis should be performed on thedata vectors as a whole for best efficiency. A point-at-a-time approachused here would not normally provide competitive performance.

In a present embodiment, this problem is addressed by using multipleversions of each mathematical function and operator for the dataanalysis, with each version optimized for a particular situation. Bothversions possess the same identifier or recognized name, but the actualversion that is executed depends upon the current circumstances. Thus, afirst version of the function or operator may be optimized foron-the-fly processing, and the second version optimized forpost-processing. Each would be appropriately called if the circumstancesmatch their intended execution parameters.

FIG. 9 shows a flowchart of a process for implementing this aspect ofthe present embodiment. At 902, a first version of the function oroperator is defined. At 904, a second version of the function oroperator is defined. In one approach, each version takes the same orsimilar arguments. In operation, the defined function or operator iscalled (906). The particular version that is used is determined from theactual type of the arguments and the setting in which it is being used(908). For example, different versions for a single function or operatormay exist for post- or on-the-fly-processing, for time-aligned data (asgenerated by a circuit simulator) or non-time-aligned data (as generatedby a timing or logic simulator), for continuous- or discrete-valuedsignals, or for real and complex signals. Once the appropriate versionis identified, it is executed to perform the data analysis (910).

In effect, the user selects one function that appears independent of thedata types and setting, and through function/operator overloading, themost efficient of many possible implementations is used. Thus, functionand operator overloading is performed for the sake of efficiency evenwhen the arguments of the function or operator are seemingly identicalbetween the two versions (the choice between the multiple versions ofthe functions would be made on the basis of when the arguments weregenerated and what source they came from, not based on the underlyingdata types). This concept can also be extended to support overloadingfor functions and operators even when their argument types aredifferent. For example, there might be two versions of the FFT function,one for real-valued waveforms and the other for complex-valuedwaveforms, but they would have the same name and they would look likeone function to the user. In addition, function and operator overloadingcan be based on argument type and dimension in the context of asimulation analysis and control language.

Run Statements

A measurement can be executed using a controllable and flexible controlstatements, which in one embodiment of the invention comprise the “run”or “show” commands. In one approach, the run command is configured toexecute one or more measurements and prints a return values. The showcommand is configured to also execute one or more measurements, but itpasses any return values for graphical display (e.g., using a viewcommand). Both measurements and analyses can be executed using the runand show commands. As used for this section, analyses are predefined orbuilt-in measurements while measurements are user-defined analyses.

Described in this section is one embodiment of an approach forimplementing the run and show actions. FIG. 16 shows a flowchart of anembodiment of a process for implementing a run command according to anembodiment of the invention. at 1602, the run command is received forexecution. The run command is a statement in which the name or names ofthe measurements to execute are provided as keywords to the command. At1604, a determination is made whether multiple measurements are to beexecuted. If multiple measurements are named in the run statement, theycan be run simultaneously, e.g., by determining if multiple processors,threads, or other parallelizing mechanisms are available and employingthe available mechanism to simultaneously execute the measurements. Inaddition, individual measurements can be executed as parallel iterativeblocks (see above regarding foreach loops). Thus, the described controlstatement inherently allows parallel execution of measurements.

As explained in more detail below, conditions specify sets of one ormore changes or parameters that can be applied to a circuit forsimulation. At 1606, a determination is made whether condition(s) havebeen specified for the measurement(s). If so, then the conditions areapplied during the execution of the measurement (1608). In effect, therun command iterates one or more measurements over a set or range ofconditions. The conditions could be applied to a single measurement orto all of the named measurements. In the present embodiment., the runcommand would also add the list of conditions to the measurement resultsas an item of documentation. Once the measurement was complete, theconditions could be removed. As described below, the conditions can alsobe tracked and saved for later use.

At 1610, a determination is made whether parameter values from previousruns should be used in the present execution of the run command. In thisembodiment of the invention, when executing a run command, themeasurement parameters and results of the statement are stored. At alater execution, this allows a previous measurement with previousparameter values to be used without requiring these values to bere-specified (1612). In addition, this allows previous results to beimmediately available for comparison with present results. In oneapproach, the system can be configured to detect whether measurementhave changed and to ensure that the run command would only runmeasurements that were out-of-date.

In one implementation, the run command causes measurement results to beassigned to local variables. The run command would also attachinformation to the local variable that would allow the variable to beupdated by rerunning the measurement simply by specifying that thevariable itself be rerun. The run command reruns the previous set ofmeasurements if no new ones are specified.

At 1614, the measurement(s) are executed. As noted above, multiplemeasurements can be run simultaneously. It is contemplated that themeasurements can be run in the background, such that the user need notwait for the measurements to finish before proceeding.

The results/return values are thereafter printed (1616). In oneapproach, the run command would arrange the results in the form of afamily for easy viewing. Plotting a result from a measurement that isstill running causes partial results to be displayed and thenautomatically periodically updated until the measurement completes.

At 1618, the results and parameters for the present run can be storedfor future use. This allows the current measurement results to becompared against previous measurement results. The comparison can beperformed automatically, or under manual control. Moreover, storing thisinformation this allows the parameters to be re-used without beingspecified at the later run.

As explained in more detail below, aliases can be created formeasurements. In one embodiment of the invention, the run command can beconfigured to create aliased measurements on the fly. The aliases couldinclude conditions or comparisons.

The show command is used in the same way as the run command, and acceptsall of the same keywords. However, besides printing the return valuesfrom the measurements it runs, it also causes them to be viewed in agraphical display. In this way, the show command is the run commandcombined with the view command. The results from each measurement aredisplayed in their own window, and if the results are already displayed,the results are updated. The show command will also plot previousresults along with the current measurement results for comparison. Inaddition, with the show command, the new and comparison results can beboth printed and graphically displayed.

The following will describe illustrative examples of syntax and formatfor the run command. Consider a run command that executes twomeasurements. If the appropriate mechanisms are available, themeasurements are run simultaneously, and execution continues from therun command once all measurements have completed. For example, if nf andcp are two measurements, then

run nf, cp

runs both and sets the active dataset to be the primary datasetgenerated by the first measurement listed, in this case nf. Parameterscan be passed into the measurements.

run nf(start=55 MHz, stop=26 GHz), cp(start=−20_dBm, stop=10_dBm)

Once values have been passed into measurements, the parameters retainthese values for later runs from the same context. Thus, once start andstop have been specified for nf and cp as above,

run nf, cp

will rerun these measurements with the specified values rather than withthe default values. If you would like to temporarily set themeasurements parameters, but not have the changes persist, add thekeyword trial after the measurement where the parameters are specified.Thus,

run cp(stop=20_dBm) trial

would result in cp running with stop=20_dBm, but the next time it wasrun stop again returns to its value of 10_dBm that was specifiedpreviously.To specify that the measurements should be run with a default parametervalue, pass null for the value:

run nf(start='null, stop='null), cp(start='null, stop='null)

Or one could simply reset the measurements and then rerun them:

reset nf, cp

run nf, cp

A run command with no arguments repeats the most recent run command fromthe same context.

Thus,

run

reruns the nf and cp measurements.

Keywords are used to modify the behavior of the measurements. Keywordscan be associated with a single measurements, or all the measurementsspecified on the run command. The placement of the keyword relative tothe comma that separates the measurements determines the scope of itsinfluence. If the keyword is place between the measurement and itscomma, then it applies only to that measurement. If it is placed afterthe last comma, it applies to all measurements.

The with keyword specifies conditions that persist for the duration ofthe measurement. A single assignment or condition is specified after thewith keyword. If multiple conditions are to be applied, use multiplewith keywords.

run nf with temp=0, cp with slow

run nf, cp, with temp=0 with slow

The foreach keyword is used to specify a series of conditions underwhich the measurements are applied.

run nf, cp, foreach {fast, typ, slow}

repeats nf and cp for each corner where fast, typ, and slow are assumedto be predefined condition sets. Equivalently, one could use

run nf, cp, foreach corner

where corner is assumed to be a set of equivalent condition setscombined together by a corners statement. One could also use the foreachkeyword to iterate the measurements over a list of parameter values.

run nf, cp, foreach VDD from {3,4,5}

Similarly, one could also use the swp function to sweep the measurementsover a range of parameter values.

run nf, cp, foreach VDD from swp(start=3, stop=5, step=1)

Results for a foreach loop are arranged as a family. So,

view out@4

plots the results for out with VDD=4 V.

Multiple foreach lists can be specified, in which the lists aretraversed in combination, with the list given first becoming theinnermost.

run nf, cp, foreach VDD from {3,4,5} foreach {fast, typ, slow}

The conditions are applied in the order given, so with

run nf, cp, foreach {fast, typ, slow} with temp=25

will all be run with temp=25 regardless of whether temp is set in acorner because with temp=25 occurs later on the command.

The results of a measurement can be compared with previous results usingthe vs keyword. The name of a data hierarchy would follow the keyword

run cp vs prey

current prev change % change cp 8 dBm @ −2 dBm 10 dBm −2 dBm −20%In this case, the cp measurement is run and its return value is printedalong with the one saved away in the prey data hierarchy.

It is also possible to define measurement aliases on the fly by addingthe as keyword when running a measurement with a run or show command.

run ac(center=1 MHz, span=1 kHz) as pb

run ac(start=1_Hz, stop=10 MHz) as sb

In these cases, the alias is created before the analyses are run, and sothe default parameter values for the base AC analysis are not affected.The as keyword must follow a measurement, and applies only to thatmeasurement. If the as keyword follows the with, foreach, or vs keyword,the keyword also become associated with the alias. Thus, if the alias isrerun, the keywords are automatically applied. For example,

run dc with temp=50 as dcat50

then

run dcat50

reruns the dc analysis, again with temp=50.

A run command with no arguments will repeat the last run command. Inthis case, any keywords specified apply to all measurements.

run of sparams ip3

run with Ibias=100 uA vs prey

run with Ibias=150 uA

In this example, the three measurements from the first run command arererun in the second, this time with Ibias=100 uA while comparing withthe previous results. Then in the third run command, the measurementsare again repeated with Ibias=150 ua, and the results are again comparedagainst the previous results.

The return value of a measurement can be made available in the datasetproduced by the measurement. But it can also be available to be storedto a local variable. To do so, assign the value to the variable on therun command.

point cp1

run cp1=cp

If a measurement returns more than one value, it is returned as a group.

Besides assigning the value to cp1, the run command also attaches anattribute, meas=cp. Now, one can rerun the above simply with

run cp1

In this case, MDL recognizes cp1 as a local variable and looks for themeas attribute, and if present runs that measurement and stores theresult in cp1.

Alias Measurements and Functions

Measurements may be shared among multiple users. However, while aparticular shared measurement might do what the user needs, it may notprovide an optimum interface. It may use a cumbersome set of parametersor may not produce the exact form of the desired result. Modifying themeasurement to suit the user's needs may be problematic. If the userwere to modify the measurement to be nonstandard, extensive support andmaintenance for the measurement may be required at additional expenseand effort, e.g., if updated versions of the measurement is released,then the later release would also have to be modified. Also, suchmodifications could be intimidating for some users if the measurementsare complex or subtle.

According to one embodiment, a measurement alias provides a way tomodify the interface or functionality of an existing measurement withoutmodifying the measurement itself. A measurement alias is a definedconstruct that encapsulates the underlying measurement, allowing changesto various aspects of the measurement such as its name, default valuesof the parameters, names and meanings of parameters, and to the resultsproduced.

FIG. 10 shows a flowchart of a method for implementing a measurementalias according to an embodiment of the invention. At 1002, theunderlying base measurement is defined. At 1004, the measurement aliasis defined. As noted, the measurement alias references the underlyingmeasurement, but may change one or more aspects of the underlyingmeasurement. For example, new parameters can be declared for themeasurement alias, and these newly declared parameters can be used tospecify the parameters of the underlying measurement. When the alias iscalled (1006), it is the underlying measurement that gets executed(1010). However, since the alias might have redefined certainparameters, the declared parameters of the alias are first mapped to theparameters of the underlying measurement (1008). Any unspecifiedparameters are inherited from the base measurement.

In one embodiment of the invention, measurement alias definitions aresimilar in form to measurement definitions, but includes a call to theunderlying measurement. In the embodied language, the alias definitionis preceded with the keyword alias. In one embodiment, there must be oneand only one run statement, and that it may run one and only one submeasurement. For example, consider the following example of ameasurement alias:

alias measurement transient { input real tstop input real tstart inputreal tstep input real delmax run tran(stop = tstop, start = tstart, step= tstep, maxstep = delmax) }

In this example, the new measurement alias “transient” will call theunderlying measurement “tran”. Regardless of the names or quantity ofthe input parameters that may be needed for the underlying measurement,the interface for this measurement alias only defines the four inputparameters in this alias definition (e.g., tstop, tstart, tstep, anddelmax). These parameters are mapped to the appropriate parameters forthe base measurement when the run statement is executed (the runstatement is explained in more detail below).

Once defined, a measurement alias cannot be distinguished from ameasurement by end-users. Thus, one could use:

run transient(tstop=10 ns)

to run the alias.

In this embodiment, if the parameters are given by order rather than bename, the alias parameters are expected first, followed by thoseparameters of the base measurement that have not been overridden. Onecould simply encapsulate tran in a traditional measurement and achievethe same basic effect as an alias. In an embodiment, an alias has someor all of the following properties. First, an alias supports thespecified parameters along with any unbound parameters for the baseanalysis. Second, the help description for the alias is built upstarting from the help description for the base measurement. Inaddition, the name given to the dataset is taken from the alias ratherthan the base analysis. Thus, the dataset produced by tran is exportedfrom transient without inserting an extra level of hierarchy.Expressions given with an alias measurement could run concurrently withthe base measurement. Moreover, conditions can be associated withmeasurement aliases. Additionally, comparisons can be associated withmeasurement aliases. Some or all of these properties differentiate analias from a measurement.

Measurement aliases have many uses. For example, one could use it tochange the interface of an existing measurement to make it morecomfortable for the user or easier to use. Consider this example wherethe meaning of the lin and log parameters are changed to make them meansteps rather than points:

alias measurement ac { input int lin input int log run ac (lin = lin+1,log = log+1) }Notice that the alias may have the same name as the base measurement, inwhich case in one embodiment, the base measurement is hidden and cannotbe called directly after being aliased unless the alias is deleted. Orconsider the case where a user wants a parameter value to track a localvariable:

alias measurement sineResp { extern real period run tran(stop =3*period) }There is the case where the user wants to add some output processing andplotting.

alias measurement histogram { input inst clk input int bits runsineResp(clk=clk, bits=bits) export signal smpled = in @ smpl(clk:delay,clk:period) output int@ minhits = min(histogram(smpld, pow(2, bits)) - -“hits” - - “Hits per bin” view minhits }Finally, it is possible to associate conditions and comparisons with analias. For example, as shown in the following:

alias measurement opAt50 { run dc } with temp=50 and alias measurementopVsPrev { run dc } vs prevA short hand form for defining measurement alias is available to allowquick change to the name of a measurement and specify fixed values forone or more parameters. For example, consider the case of a filterdesigner that would like to routinely run two different AC analyses, oneover the passband of the filter, and the other over the stopband of thefilter. It would be convenient to define two measurement aliases for theAC analysis as follows

alias measurement pb=ac(center=1 MHz, span=1 kHz)

alias measurement sb=ac(start=1 Hz, stop=10 MHz)

One could now measure both the passband and the stop band of the filterusing

run pb, sb

Changing the name of a measurement also changes the name of the datasetit produces. So the re-sults of these two measurements can be plottedwith

view pb−>out sb−>out.

Interactive Datasheets

In conventional approaches, simulation result data is typicallydisplayed in a graphical format. The simulation parameters and resultsare not typically displayed as true “numbers.” An embodiment of thepresent invention provides an advanced non-graphical approach todisplaying data. FIG. 8 shows an example of a single form that a usercould efficiently use during the “simulate, view, update” loop. Itallows the user to modify the local version of the circuit that ismaintained by the simulator during a session, and it also allows thesimulation output parameters to be displayed in the form of a Data Sheet271, as shown in the illustrative example of FIG. 8. A significantadvantage of this approach over the graphical approach is that a greaterdata density can be presented to the user in this approach.

Section 270 is an edit section that allows changes to be made to theinput parameters. The user can be given the opportunity to select valuesfrom lists, by browsing the circuit, or from the schematic asappropriate. The user can either set individual circuit, instance,model, and simulator paramters, or can set them as a group by usingcorners (altergroup) or by reverting to an earlier parameter set(conditions).

Section 275 is a history section, which allows the user to managepreviously generated results. The circuit conditions associated withevery simulation made in the current run is available along with thesimulation result. In addition, any conditions/output parameter pairthat is named is also available even though it may have been run in aprevious session. From this section one can view the contexts and outputparameters, one can save output parameters, one can set the referenceoutput parameters, and one can compare two condition sets. Thisinterface can be used to manage the history features described withrespect to FIGS. 3 and 4.

Section 280 is a results section that lists the user definedspecifications. Each specification can be associated with a measurementthat is assumed to return one or more scalar values and can generate oneor more graphs. The user can associate goals and bounds with eachmeasured value. The goals can be used for optimization and the boundscan be used with Monte Carlo to compute yeilds. Selecting a scalarmeasurement result brings up the associated graph for that result. Inthis way, if a number looks suspicious, the user can quickly plot thedata used when computing the measurement result, and show both theresult and the data.

Each field can be presented with a given background color and a textcolor, both of which may be significant. The background color conveyswhether the information in the field is up-do-date. For example, theblue color may be used to convey that the data is up-do-date, gray toimpliy that it is not, and aqua color to imply that the data is userspecified. When a simulation is run, if the circuit conditions have beenchanged, the background for all measurements turns gray indicating thatthe results are now out-of-date. Then as the measurements complete, theassociated background turns blue, which gives the user additionalfeedback on the progress of the simulation. In the example, some fieldsare shown as green. That signifies an immediate action button. Greenfields in the Graphs and Tables column can be used to indicate that thegraph or table should be displayed immediately (out-of-date or not) whenthe button is pressed. A text color of green implies that the data iswithin the goals, a text color of yellow implies that it is within thebounds but not the goals, a text color of red implies that it is outsidethe bounds, and a text color of white indicates that there are no boundsassociated with the field. Many fields have toggle buttons thatindicates which fields should be updated when the user requests anupdate, which is done using the simulate section. The fields in theGraphs and Tables column also have toggle buttons that are used toindicate that the graphs should be displayed when the measurement valueis updated.

The columns used in this section can be user configurable. The columnsshown are merely examples, as other columns can be employed in theinvention. Such additional columns may include, for example, ImprovementFrom Previous, % Improvement From Previous, Improvement From Reference,% Improvement From Reference, Reference, Best, Conditions, SelectedParameter Value, Conditions, % Deviation, Selected MeasurementParameters, and Progress Towards Goal Bar. The user can specify whichcolumns are presented and in what order. The Improvement from Previouscolumns report on the change from the the most recent results available.The Reference and Improvement from Reference report on the result andchange in results from the designated reference conditions. TheConditions and Selected Parameter Value are used to indicate theconditions of the circuit at the time the measurements were made. Thisdiffers from the With column which enforces conditions. For example,Vdd=3V in the Selected Parameter Value implies that Vdd happened to beset to 3V when the measurement was made, whereas if it were found in theWith column, it indicates that the value of Vdd was overridden and setto 3V for the duration of the measurement. Typically the With column isused to set the supply voltage, the temperature, the load, or theprocess corner as variants of specific measurements. One could alsoconsider removing the units from each column and having an additionalUnits column. The form can also be implemented to create column titlesfrom corners, so users can see results for fast, typical, and slow indifferent columns.

The actual measurements reported on in this section are chosen using theConfigure button on the Results header bar, as are the preferredcolumns. Specific rows and columns can be hidden when not needed to makethe form smaller. Along with measurements, rows in the table can bededicated to headers/separators. In this way the different types ofspecs can be grouped and easily distinguished.

Section 285 is the run section and contains interface control objects,e.g., buttons, that when actuated, cause the data in the section 280 tobe updated. The user can define their own buttons, each of which wouldresult in a pre-specified group of output parameters being updated.These user-defined buttons are shown in the second row. The user wouldright-click in order to access special run options, such as running inbackground or running at a set time.

Section 290 is a status section that shows what the simulator iscurrently working on and what is the estimated time to completion. Thisis shown using both the Done in field as well as a progress bar. Alsoshown are the messages generated by the simulator with filters andsuppressors that make it easier to deal with large numbers of messages.The Show button is used to highlight nodes/instances mentioned in themessage and the Hide button is use to suppress messages that the userhas identified as being uninteresting.

In each of the sections 270, 275, 280, 285, and 290 the user can selectthe each field to additional capabilities of each field. For example, aright-click in the Name field allows the the user to specify theparameters of the measurement. A right-click in a field associated witha measured value gives access to the conditions of the circuit when themeasurement was made and gives the user the option of setting orreverting the current state of the circuit back to that conditions. Aright-click can also be used to configure the field, set displayresolution and format, set bounds as in the case of the Yield fields,select the parameter in the Selected Parameter Value or Conditionscolumns, etc.

The user interface in FIG. 8, can be utilized with the measurements andfunctions. However, the functions can be run from command lineinterfaces or the like. Further, the user interface described withrespect to FIG. 8 can be changes by using different field and sectionnames and by adding and subtracting fields and sections.

Templates

In a command environment, it is often desirable to allow end-users touse and implement measurements without requiring the user to learn thecomplexities of programming the measurements or to learn the underlyingdetails of a measurement implementation for a particular result set thathas been achieved. In particular, it is useful to provide a method ofallowing a user/system to identify what is being simulated and plottedalong with associated changes. The user can then refine the display andstore the changes. Regardless of intervening simulations, the usershould be able to re-display the prior plot along with any changes.

An embodiment of the invention allows implementation of templates thatare associated with graphs and tables based upon the data beingdisplayed. These templates can be created using a display tool, whichallows the users to customize the way their results are displayed usinga GUI even though they may use a text user interface (“TUI”) to createthe display. Once it is possible to associate a view or show commandwith a window that is displaying its results, then it also becomespossible to update the results in a variety of ways. One could simplyre-issue the view or show command and have the currently displayedresults updated, or the new results can be overlaid, or a new window canbe created for the new results. Therefore, automatic updating ofexisting graphs can be performed based upon the data and identifyingoptions for automatically updating the template.

One embodiment of the invention provides a predefined collection ofgraph and table templates that are useful in particular situations.These view types can be collected and configured to normally allow oneto get close to displaying the data in a way that is appropriate for thedata being displayed.

FIG. 12 shows a flowchart of a process for using templates according toan embodiment of the invention. At 1202, one or more graph or tabletemplates are defined for the system. The template collection couldcomprise a set of default templates for the system. In addition, thetemplate might have resulted from a prior iteration of the process, inwhich the user has interactively refined a previously defined graph ortable and then store the changes to a view template file. This file canbe keyed to the plot or table by the names of the traces being displayed(1204). The next time that the same traces are displayed, the templateis activated and the plot or table is recreated with the updatedsettings.

At 1206, an activity occurs that causes the type of display activity forusing a view template. This type of activity may be, for example, a viewor plot command issued by the user. A determination is made whether orwhich template is associated with the display activity at issue (1208).If not, then default display handling activities are performed (1212).

Otherwise, the specific template associated with the display activity ordata is identified (1210). For a template to be automatically activatedin one embodiment, the location, number, and name of traces plottedshould match those used when the plot or table template was generated.In one approach, to avoid using a template that would otherwise be usedautomatically, the user or system would specify either the type ortemplate argument with the plot command. The type argument indicatesthat a predefined template should be used. The template argumentindicates that a previously generated template file that is not an exactmatch should be used. In this case, the traces specified on the viewcommand are matched up with those present when the template was createdby the order in which they are given. The view tool is used to createthese templates, and it also has the ability to promote them to viewtypes.

At 1214, the identified template is used to display the data. Oncedisplayed, the user can interactively refine the graph or table and thenstore the changes in to a view template file (1216 and 1218).

Conditions and Corners

The present invention can be configured to provide the notions of thebase circuit and of conditions. The base circuit is the circuit at thepoint when it was opened. Conditions represent changes made to the basecircuit. This section presents an approach for implementing conditionsand corners according to one embodiment of the invention.

FIG. 14 shows an architecture for managing conditions according to oneembodiment of the invention. In this approach, conditions are managed bydynamically keeping track of the condition set for a circuit orcondition sets for previous results. For a circuit 1402, a datastructure 1400 tracks the condition set for that circuit 1402. In theexample of FIG. 14, the data structure not only maintains and tracks thecurrent condition set 1404 for the circuit, but also tracks the priorchain of condition sets 1406 for that circuit. Thus, a history ofchanges can be tracked for a particular circuit. In one embodiment, theconditions can be stored with the simulation results. This is in sharpcontrast to conventional approaches in which only results are stored.

Tracking conditions sets for a circuit provides numerous advantages. Forexample, this type of approach allows one to quickly revert a circuit toa previous conditions set, e.g., if a non-optimal change or set ofchanges has been made to a circuit. Moreover, storing condition setsallows the multiple condition sets to be compared. With respect to anetlist, tracking condition sets allow one to interactively update andmodify a netlist with a particular combination of conditions.

In one approach, a condition set can be explicitly defined. For example,a manual or automated process can be performed to create a new conditionset for a circuit. This allows one to capture a condition or set ofconditions so that they are quickly applied to a circuit. Moreover, thisallows a designer to capture the conditions that one would want tocompare a circuit or result set against. This also allows a designer todefine a corner set and run measurements over a corner (corners areexplained in more detail below).

In one embodiment, one can only change values, not topology. Thus,topological changes would call for the circuit to be edited (by editingeither the schematic or the netlist) and then be reopened. Each resulthas a set of conditions that can be displayed, compared, or reverted to.One can also apply conditions during the course of a measurement.Changes can be made to the circuit by directly assigning values todesign parameter.

In an embodiment, a named sets of conditions can be created and appliedas a group. Named sets of conditions are contained in groups, so tocreate a named set of conditions, or condition set, one should firstcreate a group. In one approach, a comparison can be made for thecurrent and original conditions against the conditions of a named set, ameasurement, a dataset, or a file. One can also compare the conditionsassociated with two objects.

Condition sets are helpful if there are a fixed set of conditions underwhich the circuit is to be tested. For example, a condition set couldinclude a temperature, one or more supply voltages, and one or moremodel groups.

It is possible to group condition sets into a set of ‘corners’. A set ofcorners is a set of condition sets that completely overlap. In otherwords, a parameter that is set in one corner will be set in all.Condition sets that completely overlap can be considered equivalent. Inon embodiment, only equivalent condition sets can be combined togetherinto a corner set. Model groups are equivalent if they apply to the samecomponents.

Condition sets can be attached to measurements, in which case they willbe applied whenever that measurement is run for the duration of thatmeasurement. Conditions can also be written to a file. A measurement iscan be tagged to indicate the conditions should be taken from themeasurement rather than the dataset of the same name. There are severalways of applying conditions when running measurements. The first issimply to apply the conditions to the circuit and run the measurement.These conditions will be the active set when subsequent measurements arerun unless they have been overridden on a particular measurement. Thesecond is simply to attach the conditions to the measurement. In thisway, whenever the measurement is run with the conditions applied inaddition to what ever changes were made to the base circuit. Finally, itis possible to apply conditions on a single run of a measurement.

These conditions are applied along with those attached to themeasurement and to those already made to the base circuit, but theyoverride those that apply to the same parameters. One can run thecircuit over a set of condition sets using the run command.

The corners command is used to register a set of equivalent conditionsets as a set of corners. As noted above, condition sets are equivalentif they each associate values to exactly the same set of designvariables. In one embodiment, corners are applied in the same manner asnamed condition sets.

Once registered, the names of the corner are printed rather than theindividual value of each design variable when the conditions areprinted. If one of the value of a design variable that is a member of acorner is overridden, then in addition, its value would be printed alongwith the corner.

Therefore, shown is a method and system for defining condition sets andapplying them to measurements. In addition, corner sets can be definedand measurements run over corners. As stated, the conditions can bestored with the results. Moreover, the system can revert to a previouscondition. Multiple condition sets can be compared as described above.The netlist can also be updated with conditions. In on embodiment,conditions are managed on the fly, by keeping track of current conditionset for the circuit, or condition sets associated with previous results.

Probes

Consistent with embodiments of the invention, measurements can bewritten to be heavily reused. The idea is that there can be one versionof a measurement in the library, and it can handle all common variationsin the circuits to which it is expected to be applied. In oneembodiment, it is the role of the specified parameters to allow themeasurement to be customized to support this. The parameters allow theuser to specify the analysis parameters (e.g., starting points, stoppingpoints, etc.), stimulus parameters (e.g., source names, amplitude,frequency, etc.) and observation points (e.g., point of observation,observation variable and mode, etc.). Probes are functions/operationsthat can be flexibly used with measurements to handle desired variationsin applying the measurement. The probe functions take nodes, terminals,or instances and allow any type of observation to be specified.

To illustrate the use of probes in one embodiment, consider the device1500 shown in FIG. 15. Assume that it is desired to analyze theelectrical properties at P_(o) and N_(o). One approach is to perform“node passing” for a measurement for nodes P_(o) and N_(o), which canprovide the voltage difference between these nodes. However, passingnodes is restrictive in that it cannot support other types ofobservations, e.g., current. To support other observations, e.g., bothdifferential and common-mode, another parameter would have to be passedin.

In the present embodiment, a probe is employed as a parameter to ameasurement. Because the probe function is defined external to themeasurement, it can be flexibly defined to perform any desiredfunctionality without requiring change to the measurement. In addition,the probe can be generically implemented and used in conjunction withany number of different observations. This allows any number or type ofnon-traditional signals, results, or observation data to be calculated,merely by implementing a probe directed to the desiredoperation/observation.

Therefore, for the example shown in FIG. 15, a probe can be defined tomeasure any desired property with respect to nodes P_(o) and N_(o),without requiring nodes to be passed in to the measurement. The probefunction would take arguments that describe the observation point andconvert it to a pointer which is passed into the measurement through aprobe parameter. The probe parameter passes a pointer to a location inthe circuit where some signal could be measured or input. The locationof a probe can be associated with any object, e.g., a node, a terminal,a pair of nodes, a pair of terminals, a node and a terminal, or aninstance. To be used with probe functions, an instance should beassociated with either one or two terminals, or be in the definition ofthe instance itself, the default voltage and current should also bedefined.

FIG. 13 shows a flowchart of a process for using probes according to anembodiment of the invention. At 1302, a measurement for a given set offunctionality is defined. As part of that definition, a probe can bedefined that is associated with that measurement (1304). Variousillustrative approaches for defining probes are described in more detailbelow. The definition process allows a probe to be used as a parameterto a measurement (1306). It is the role of probes in this embodiment toallow the observation points to be passed into measurements asparameters in a way that is completely generic (1308). Without probes,one would normally have to pass nodes into the measurement in order tomeasure voltage, and terminals in to measure current. But this mayconstrain the measurement. By using probes, the measurement simplyaccepts a probe as a parameter. The possible variations in what can beobserved are handled by probe functions.

Probe functions take nodes, terminals, or instances and perhaps a modeflag, and allow any type of observation to be specified. In particular,the observation point can be associated with a node, a terminal, a pairof nodes, a pair of terminals, a node and a terminal, or an instance. Inone embodiment, to be used with probe functions, the instances may haveeither one or two terminals, or in the definition of the instanceitself, the default voltage and current are defined. In anotherembodiment, an indication is provided (e.g., by the user) as to whichsignal is of interest or by defining a parameter to this effect. Probeliterals can also be created using probe functions.

This section provides illustrative examples of probe functions showingsyntax, operational, usage, and behavior for an embodiment of theinvention. Examples of defined probe functions are val, which accessesthe potential, and flow, which accesses the flow:

Examples:

-   -   val(p,n)—potential (voltage) between nodes p and n    -   val(Rload:1)—potential (voltage) from terminal Rload:1 to ground    -   flow(Rload:1)—flow (current) through terminal Rload:1    -   val(Rload)—potential (voltage) across instance Rload    -   flow(Rload)—flow (current) through instance Rload    -   val(Q1:b,Q2:b)—potential (voltage) difference between terminals        Q1:b and Q2:b    -   flow(Q1:b,Q2:b)—difference in flow (current) flowing into        terminals Q1:b and Q2:b

In addition, the circuit itself can provide additional probe functionsthat are suitable for use on particular signal types. In an embodiment,these functions are defined using Verilog natures. Electrical circuitswill normally define V and Ito be used to access the voltage andcurrent. In addition, in mixed discipline applications, other natureswill be used to define probe functions for other types of signals, suchas mechanical, thermal, optical, hydraulic, etc.

Examples:

-   -   V(p,n)—voltage between nodes p and n    -   V(Rload:1)—voltage from terminal Rload:1 to ground    -   I(Rload:1)—current through terminal Rload:1    -   V(Rload)—voltage across instance Rload    -   I(Rload)—current through instance Rload    -   V(Q1:b,Q2:b)—voltage difference between terminals Q1:b and Q2:b    -   I(Q1:b,Q2:b)—difference in current flowing into terminals Q1:b        and Q2:b

In an embodiment, applying a “flow” access function to a node or a pairof nodes is not allowed. In addition, if the probe is placed between twopoints, both points should have consistent natures (e.g., val(e,m)should not be used if e is electrical and m is mechanical). Thearguments to the probe function can either be a variable that points toan object, or the object itself.

In one approach, probe functions take additional name argument tospecify whether the desired signal is the differential signal or thecommon mode signal. If this argument is not given, then differentialmode is assumed in one embodiment. If only one node or terminal isspecified, then the signal is considered single-ended, in which caseboth differential and common modes return the same result. Otherwise,differential mode returns the difference and common mode returns theaverage.

Examples:

-   -   V(p,n,'dm)—returns V(d)−V(n)    -   V(p,n,'cm)—returns (V(d)+V(n))/2        The probe declarator is used to declare probes.

Example:

-   -   probe out=V(p,n)

The following measurement demonstrates one way in which probe functionsare use within a measurement.

measurement cp --- “Measures 1 dB compression point” { input instsrc=Pin from {port} --- “Input source” input inst load=Rout from {port,resistor} --- “Output load” input real start=−10_dBm --- “Initial inputpower level” input real stop=10_dBm from (start..) --- “Final inputpower level” input real fund=1GHz --- “Fundamental frequency” outputpoint cp --- “Compression point” export signal Pout --- “Output powerversus input power” src:type = ’sine src:freq = fund foreach src:dbmfrom swp(start=start, stop=stop) { run pss( fund=fund ) real Vfund =V(load)[1] Pout=dBm(Vfund**2/(2*load:r) “W”) }cp=compression_point(Pout,1) }

This measurement computes the output power. Notice that the user definesthe output of the circuit by passing in the load component, load. Thisgives at least two ways to compute the power. One could use either:

pwr=V(load)I(load)/2

Or

pwr=V(load)2/load:r

Here the probe allows access to the needed signals given only the nameof the load instance. If one did not have the probe function, then twonodes are passed in to get the voltage and either a terminal to get thecurrent or an instance to get the resistance. This could be morecumbersome. It may also be problematic because a user may specify theoutput inconsistently.

The following example shows another use of probes where the probe ispassed in as a parameter.

measurement setupTime --- “Measures setup time of a latch” { input instclk from {vsource} --- “Clock source” input inst d from {vsource} --- “Dinput source” input probe q --- “Output signal” input real maxTs =clk:delay from (0..) --- “Maximum expected setup time” input realmaxTsettle = maxTs from (0..) --- “Maximum expected settling time forthe output signal” output real Ts --- “Setup time” clk:delay = maxTssearch d:delay from binary(start=0, stop=maxTs, tol=10ps) { runtran(stop=maxTs + maxTsettle) Ts = latched(V(clk), V(d), q) } while (Ts!= ’null) }

When writing this measurement, one would not want to assume too muchabout the type of the output. The measurement should work for single anddifferential outputs as well as current outputs. And unlike the lastexample, a load component is not insisted upon. So instead, the userspecifies the output using a probe. The output is passed in as the probeparameter q, which is treated like a signal within the measurement. Thefollowing example shows how a measurement might be illustrativelycalled:

run setupTime( . . . , V(q), . . . )

if the output is single-ended, as follows . . .

run setupTime( . . . , V(q,qbar), . . . )

if differential, and as follows . . .

run setupTime( . . . , I(FF1:q), . . . )

if the output is a current.

At the time the measurement is called in an embodiment, there may not bea dataset, measurement point in the circuit, and it is associated withsignals in a dataset when it is evaluated (in the latched functionalwithin setupTime).

Signals

Signals are vectors of points, where a point is a pair: an ordinate andan abscissa. One uses signals to represent trajectories. This sectiondiscusses how to construct and access signals. Variable and parametertypes provide support for optional abscissa values. The abscissa is anextra value for which there is a parametric relationship. When anabscissa is attached to a value, the value to which it is attached isreferred to as the ordinate. For example, if x0 is assigned to be thevalue of a signal x at time t=0, then the abscissa t=0 would be attachedto x0, the ordinate. Generally, abscissa are attached to signals duringthe process of a sweep. It may be an implicit sweep of time, as in atransient analysis, or an explicit sweep of some parameter using aforeach statement (section on page 33 and section on page 44). Inaddition, it is possible to add abscissa values to constants andvariables that do not have them using the abscissa operator.

When applying operators to signals, the operators act on the ordinate,though they do so in the context of the abscissa. For example, if a andb are signals, then a+b causes the ordinates of a and b to be addedtogether, but only at points that correspond (points in a and b thathave the same value of the abscissa). For those points in a and b wherethere is no corresponding abscissa, interpolation is performed beforethe addition. Thus, if there is a point in a that does not have acorresponding point in b, then a corresponding point in b will becreated by interpolation before performing the addition. The sameprocess occurs for points in b for which there are no correspondingpoints in a. Sometimes it is useful to be able to strip off either theordinate or the abscissa.

The abscissa operator (e.g., “@”) is a binary operator that is used toboth assemble a signal or a point from its operands and interpolate asignal at a particular abscissa value or values. In the case where itassembles two value into a point, or two vectors into a signal, the leftoperand becomes the ordinate and the right operand becomes the abscissa.It can be used with scalar, vector, or signal operands. If the leftoperand is a signal, interpolation is performed on the left operand sothat the result conforms to the points in the right operand. In thiscase, the abscissa must be real and the range of the right operand mustbe contained within the range of the left. The binary @ operator can beused with the timer and cross functions to control the simulator andavoid interpolation.

Units can be associated with either the ordinate of the abscissa byproper placement of the units string. One can also construct a pointliteral that has units and names associated with both the ordinate andthe abscissa.

Individual values in a vector are accessed by index. Individual valuesin signals can either be accessed by either by index or by abscissa.Each value in the signal is sequentially numbered starting from 0. Thisnumber is the index. In addition, each value is associated with aparticular value of some independent parameter, such as time orfrequency. This is the abscissa. Use brackets when accessing values in asignal by index. Use the abscissa operator “@” when accessing values inan signal by abscissa. Thus, if harmonics contains the harmonics of asignal with a 1 GHz fundamental, then harmonics[1] and harmonics@1 GHzboth access the value of the first harmonic. To access a range ofvalues, use val . . . val. Thus, harmonics[[0 . . . 5]] and harmonics@[0. . . 5 GHz] extracts the values at DC and the first 5 harmonics (thebrackets represent a closed interval rather than an array access). Useval . . . to specify a range that starts at a given value and continuesup to the natural end of the range, . . . val to specify that range thatstarts at the natural beginning and continues up to a specified value,and . . . to specify the natural range. One can also use arrays as theindex or abscissa to extract a subset of values. Thus, if odd={1,3,5}then harmonics[odd] extracts the odd harmonics. Similarly, odd={1 GHz, 3GHz, 5 GHz} and harmonics@odd does the same thing.

A single index or abscissa applied to a multidimensional array pertainsto the outer most independent variable. When more than one indexoperation (using either “[ ]” or “@”) is applied together, eachoperation is associated with a dimension before the operation occurs.Thus, it is an error to apply more indexing operations than there aredimensions in the array. The indices and abscissas apply from theoutside in and can be freely mixed. For example, if the measurement thatgenerated the harmonics vector is swept over three temperatures, −25,25, 75, then harmonics[2][1], harmonics[2]@1 GHz, harmonics@75[1], andharmonics@75@1 GHz, all represent the first harmonic at 75 C. To accessthe value of a first harmonic at all temperatures, use harmonics[[ . . .]][1], harmonics[ . . . ]@[1], harmonics@[ . . . ][1], or harmonics@[ .. . ]@1 GHz.

Note that associating the index to the dimension prevents one fromapplying more than one indexing operation to the same dimension at onetime. This is usually what is desired, however there are two ways tocircumvent this and apply more than one indexing operation to the samedimension, either by using an intermediate variable or by usingparenthesis to group the indexing operations.

CONCLUSION

Therefore, described are inventions for implementing simulation for acircuit. The present inventions overcome several problems thattraditional simulation scripting languages suffer. The present inventionmay be embodied as any combination of software, hardware, or manualoperations. In one specific embodiment, the invention is embodied as anelectronic design automation (“EDA”) command language for performingcircuit simulation. Appendix A provides more details regarding anembodiment of an EDA command language for implementing the inventions.

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Forexample, the above-described process flows are described with referenceto a particular ordering of process actions. However, the ordering andcontents of many of the described process actions may be changed withoutaffecting the scope or operation of the invention. Also, while specificinstruction syntax is used to exemplify the concepts and processesdisclosed herein, e.g. cond for setting conditions, run for running ameasurement or function, any other syntax that applies similar rules maybe used without departing from the scope and spirit of the embodimentsdisclosed herein. The specification and drawings are, accordingly, to beregarded in an illustrative rather than restrictive sense.

1. A computer implemented method for executing a measurement,comprising: using a computing system that comprises at least oneprocessor and is programmed or configured for performing a processcomprising: receiving a statement to run a measurement; determiningwhether multiple measurements are to be executed; identifying whetherconditions are specified; determining whether previous values are to beused; executing one or more measurements; and generating results.
 2. Thecomputer implemented method of claim 1, in which the method is executedfrom a scripting language.
 3. The computer implemented method of claim1, in which the conditions specify sets of one or more changes orparameters applied to a circuit or simulator.
 4. The computerimplemented method of claim 1, in which, if conditions are specified,the conditions are applied to the one or more measurements.
 5. Thecomputer implemented method of claim 1, in which the measurementparameters are stored for a future execution of the one or moremeasurements.
 6. The computer implemented method of claim 5, in whichthe stored measurement parameters are identified when determiningwhether to use previous values.
 7. The computer implemented method ofclaim 1, in which the method is embodied as a RUN command.
 8. Thecomputer implemented method of claim 1, further comprising graphicallydisplaying the results.
 9. The computer implemented method of claim 8,in which the method is embodies as a SHOW command.
 10. The computerimplemented method of claim 8, in which previous results are plottedalong with the current measurement results for comparison.
 11. Thecomputer implemented method of claim 1, where conditions over whichmeasurements are run taken from either a statically or dynamicallygenerated list.
 12. The computer implemented method of claim 11, inwhich the list is a collection of corner cases.
 13. The computerimplemented method of claim 1, in which the one or more measurements aresimultaneously executed using multiple processing units.
 14. Thecomputer implemented method of claim 1, in which previous parametervalues are not re-specified unless their values have changed.
 15. Thecomputer implemented method of claim 14, in which previous result areavailable for comparison.
 16. The computer implemented method of claim1, in which the conditions could be applied to either a singlemeasurement or multiple measurements.
 17. The computer implementedmethod of claim 1, in which, once the one or more measurement arecompletely executed, the conditions would be removed.
 18. The computerimplemented method of claim 1, in which a list of conditions is added tothe results as documentation.
 19. The computer implemented method ofclaim 1, in which the one or more measurement iterate over a range ofconditions.
 20. The computer implemented method of claim 1, in which theresults are arranged in the form of a family.
 21. The computerimplemented method of claim 1, in which aliased measurements arecreated.
 22. The computer implemented method of claim 21, in which thealiased measurements are created on the fly.
 23. The computerimplemented method of claim 21, in which the aliased measurementsinclude conditions or comparisons.
 24. The computer implemented methodof claim 1, in which current measurement results are compared toprevious results.
 25. The computer implemented method of claim 24, inwhich the comparison is performed either manually or automatically. 26.The computer implemented method of claim 1, in which the results areassigned to local variables.
 27. The computer implemented method ofclaim 26, in which information is attached to the local variable thatallow the variable to be updated by rerunning the measurement byspecifying that the variable itself be rerun.
 28. The computerimplemented method of claim 1, in which previous set of measurements areused if no new sets are specified.
 29. The computer implemented methodof claim 1, in which the one or more measurements that are executed areout-of-date.
 30. The computer implemented method of claim 1, furthercomprising: in which the following are performed to implement themeasurement alias construct: implementing a measurement alias constructby performing at least: defining an underlying measurement; defining ameasurement alias; and executing the underlying measurement when themeasurement alias is called.
 31. The computer implemented method ofclaim 30, in which the measurement alias references the underlyingmeasurement.
 32. The computer implemented method of claim 31, furthercomprising declaring one or more new parameters that comprise one ormore mapped parameters of the underlying measurement.
 33. The computerimplemented method of claim 30, further comprising identifying themeasurement alias construct by using at least a keyword alias.
 34. Thecomputer implemented method of claim 30, in which a single measurementalias construct runs one sub-measurement, wherein the sub-measurementrefers to a base measurement for the measurement alias.
 35. The computerimplemented method of claim 30, in which the measurement alias constructsupports one or more parameters declared in a definition of themeasurement alias along with one or more unbound parameters from theunderlying measurement.
 36. The computer implemented method of claim 30,further comprising building a help description for the measurement aliasconstruct based at least in part upon a help description for theunderlying measurement.
 37. The computer implemented method of claim 30,further comprising exporting a dataset produced by the measurement aliaswithout inserting an extra level of hierarchy.
 38. The computerimplemented method of claim 37, further comprising identifying a nameassociated with the dataset from the measurement alias rather than fromthe underlying measurement.
 39. The computer implemented method of claim30, in which the measurement alias construct allows one or moreexpressions given to the measurement alias to run concurrently with theunderlying measurement.
 40. The computer implemented method of claim 30,in which one or more keywords are associated with the measurement aliasconstruct.
 41. The computer implemented method of claim 30, in which themeasurement alias includes a set of conditions.
 42. The computerimplemented method of claim 30, in which the measurement alias includesa directive to compare measured results against previously measuredresults.
 43. The computer implemented method of claim 30, in which themeasurement alias includes a directive to run the measurement repeatedlyover a range of parameter values or corners.
 44. The computerimplemented method of claim 1, further comprising: implementing a probe,wherein the probe identifies a quantity in the circuit to be observed.45. The computer implemented method of claim 44, in which the act ofimplementing the probe comprises: defining a measurement for a set offunctionality; defining a probe associated with the measurement; usingthe probe as a parameter to the measurement; and passing one or moreobservation points into the measurement using the probe.
 46. Thecomputer implemented method of claim 44, in which the probe is passedinto a measurement.
 47. The computer implemented method of claim 44, inwhich the probe is converted into one or more signals when the probe isevaluated.
 48. The computer implemented method of claim 44, in which theprobe observes an absolute voltage of a node or terminal.
 49. Thecomputer implemented method of claim 44, in which the probe observesvoltage between two nodes, two terminals, or between a node andterminal.
 50. The computer implemented method of claim 49, in which theprobe observes a differential voltage.
 51. The computer implementedmethod of claim 49, in which the probe observes a common-mode voltage.52. The computer implemented method of claim 44, in which the probeobserves a current.
 53. The computer implemented method of claim 52, inwhich the probe observes the current through a terminal.
 54. Thecomputer implemented method of claim 52, in which the probe observes adifferential current between two terminals.
 55. The computer implementedmethod of claim 52, in which the probe observes a common-mode current.56. The computer implemented method of claim 44, in which the probeobserves either the voltage on or the current through an instance. 57.The computer implemented method of claim 44, in which the probecomprises a name that adapts to a nature of one or more signals beingaccessed.
 58. The computer implemented method of claim 57, in which aprobe function for a voltage is named V.
 59. The computer implementedmethod of claim 57, in which a probe function for a current is named I.60. An apparatus for executing a measurement, comprising: at least oneprocessor that is programmed or configured for performing a processcomprising: receiving a statement to run a measurement; determiningwhether multiple measurements are to be executed; identifying whetherconditions are specified; determining whether previous values are to beused; executing one or more measurements; and generating results. 61.The apparatus of claim 60, in which the process performed by the atleast one processor further comprises: implementing a measurement aliasconstruct by performing at least: defining an underlying measurement;defining a measurement alias; and executing the underlying measurementwhen the measurement alias is called.
 62. The apparatus of claim 61, inwhich the process performed by the at least one processor furthercomprises: declaring one or more new parameters that comprise one ormore mapped parameters of the underlying measurement.
 63. The apparatusof claim 61, in which the process performed by the at least oneprocessor further comprises: identifying the measurement alias constructby using at least a keyword alias.
 64. The apparatus of claim 61, inwhich the process performed by the at least one processor furthercomprises: building a help description for the measurement aliasconstruct based at least in part upon a help description for theunderlying measurement.
 65. The apparatus of claim 61, in which theprocess performed by the at least one processor further comprises atleast one of: exporting a dataset produced by the measurement aliaswithout inserting an extra level of hierarchy; and identifying a nameassociated with the dataset from the measurement alias rather than fromthe underlying measurement.
 66. An article of manufacture comprising acomputer readable storage medium storing thereupon a sequence ofinstructions which, when executed by at least one processor of acomputing system, causes the at least one processor to perform a processfor executing a measurement, the process comprising: receiving astatement to run a measurement; determining whether multiplemeasurements are to be executed; identifying whether conditions arespecified; determining whether previous values are to be used; executingone or more measurements; and generating results.
 67. The article ofmanufacture of claim 66, the process further comprising: implementing ameasurement alias construct by performing at least: defining anunderlying measurement; defining a measurement alias; and executing theunderlying measurement when the measurement alias is called.
 68. Thearticle of manufacture of claim 66, the process further comprising:declaring one or more new parameters that comprise one or more mappedparameters of the underlying measurement.
 69. The article of manufactureof claim 66, the process further comprising: identifying the measurementalias construct by using at least a keyword alias.
 70. The article ofmanufacture of claim 66, the process further comprising: building a helpdescription for the measurement alias construct based at least in partupon a help description for the underlying measurement.
 71. The articleof manufacture of claim 66, the process further comprising at least oneof: exporting a dataset produced by the measurement alias withoutinserting an extra level of hierarchy; and identifying a name associatedwith the dataset from the measurement alias rather than from theunderlying measurement.